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Search results “Models based on summarization in data mining”
Data pre processing – 1 Summarization and Cleaning Methods
 
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Project Name: e-Content generation and delivery management for student –Centric learning Project Investigator:Prof. D V L N Somayajulu
Views: 6142 Vidya-mitra
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 165162 Siraj Raval
Document Summarization PART-1: Pagerank based document summarization)
 
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This video tutorial explains, graph based document summarization system (developed by using pagerank algorithm). A java implementation of the system is also demonstrated. The supporting code for the entire demonstration is available at: https://sites.google.com/site/nirajatweb/home/technical_and_coding_stuff/textrank-and-lexrank-based-single-document-summarization
Views: 6081 Dr. Niraj Kumar
DBMS - Specialization and Generalization
 
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DBMS - Specialization and Generalization Watch more Videos at https://www.tutorialspoint.com/videotutorials/index.htm Lecture By: Mr. Arnab Chakraborty, Tutorials Point India Private Limited
CVPR18: Tutorial: Part 1: Big Data Summarization: Algorithms and Applications
 
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Organizers: Ehsan Elhamifar Amit Roy-Chowdhury Amin Karbasi Description: The increasing amounts of data in computer vision requires robust and scalable summarization tools to efficiently extract most important information from massive datasets. However, summarization involves optimization programs that are nonconvex and NP-hard, in general. While convex, nonconvex and submodular optimization have been studied intensively in mathematics, successful and effective applications of them for information summarization along with new theoretical results have recently emerged. These results, in contrast with more classical approaches, can deal with struc-tured data, nonlinear models, data nuisances and exponentially large dataset. The goal of this tutorial is to present the audi-ence with a unifying perspective of this problem, introducing the basic concepts and connecting nonconvex methods with convex sparse optimization as well as submodular optimiza-tion. The presentation of the formulations, algorithms and theoretical foundations will be complemented with applica-tions in computer vision, including video and image summari-zation, procedure learning from instructional data, pose esti-mation, active learning and more. Schedule: 1345 Overview of Summarization Algorithms: Modeling, Optimizations, Applications, Ehsan Elhamifar 1430 Submodular Optimization Methods for Summarization, Amin Karbasi 1530 Afternoon Break 1600 Sequential Data Summarization and Applications to Procedure Learning, Ehsan Elhamifar 1645 Collaborative Summarization With Side Information, Amit Roy-Chowdhury
Generalization | Database Management System
 
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This lecture describes the concept of Generalization as an enhanced feature of ER model. To ask your doubts on this topic and much more, click on this Direct Link: http://www.techtud.com/video-lecture/lecture-generalization IMPORTANT LINKS: 1) Official Website: http://www.techtud.com/ 2) Virtual GATE(for 'All India Test Series for GATE-2016'): http://virtualgate.in/login/index.php Both of the above mentioned platforms are COMPLETELY FREE, so feel free to Explore, Learn, Practice & Share! Our Social Media Links: Facebook Page: https://www.facebook.com/techtuduniversity Facebook Group: https://www.facebook.com/groups/virtualgate/ Google+ Page: https://plus.google.com/+techtud/posts Last but not the least, SUBSCRIBE our YouTube channel to stay updated about our regularly uploaded new videos.
Views: 55085 Techtud
Single and Multiple Document Summarization with Graph-based Ranking Algorithms
 
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Graph-based ranking algorithms have been traditionally and successfully used in citation analysis, social networks, and the analysis of the link-structure of the World Wide Web. In short, these algorithms provide a way of deciding on the importance of a vertex within a graph, by taking into account global information recursively computed from the entire graph, rather than relying only on local vertex-specific information. In this talk, I will present an innovative unsupervised method for extractive summarization using graph-based ranking algorithms. I will describe several ranking algorithms, and show how they can be successfully applied to the task of automatic sentence extraction. The method was evaluated in the context of both a single and multiple document summarization task, with results showing improvement over previously developed state-of-the-art systems. I will also outline a number of other NLP applications that can be addressed with graph-based ranking algorithms, including word sense disambiguation, domain classification, and keyphrase extraction.
Views: 1499 Microsoft Research
Intro to Azure ML: Cleaning & Summarizing Data
 
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Let’s understand the aggregate behavior of our features further by looking at summary statistics. Azure Machine Learning gives us easy access to mean, median, mode, min, and max. Let’s look at each measure to see what it means to the interpretation of the data. The summarize data module also gives us a count for each feature with missing values. We can then formulate a strategy for cleaning missing data. The cleaning functions used in this tutorial is not the optimal way to clean data, but we must learn to crawl before we walk. We’ll drop each row that has a missing value in our response class. Then use one of the measures of central tendency to fill in the other features median for numeric features and mode for categorical features. -- Learn more about Data Science Dojo here: https://hubs.ly/H0hD2FM0 Watch the latest video tutorials here: https://hubs.ly/H0hD2lD0 See what our past attendees are saying here: https://hubs.ly/H0hD2lF0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 5923 Data Science Dojo
HindiDocumentSummary - A Context-Based Word Indexing Model for Document Summarization
 
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Existing models for document summarization mostly use the similarity between sentences in the document to extract the most salient sentences. The documents as well as the sentences are indexed using traditional term indexing measures, which do not take the context into consideration. Therefore, the sentence similarity values remain independent of the context. In this paper, we propose a context sensitive document indexing model based on the Bernoulli model of randomness. The Bernoulli model of randomness has been used to find the probability of the co-occurrences of two terms in a large corpus. A new approach using the lexical association between terms to give a context sensitive weight to the document terms has been proposed. The resulting indexing weights are used to compute the sentence similarity matrix. The proposed sentence similarity measure has been used with the baseline graph-based ranking models for sentence extraction. Experiments have been conducted over the benchmark DUC data sets and it has been shown that the proposed Bernoulli-based sentence similarity model provides consistent improvements over the baseline IntraLink and UniformLink methods.
Views: 69 RUPAM InfoTech
Data Preprocessing Steps for Machine Learning & Data analytics
 
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#Pandas #DataPreProcessing #MachineLearning #DataAnalytics #DataScience Data Preprocessing is an important factor in deciding the accuracy of your Machine Learning model. In this tutorial, we learn why Feature Selection , Feature Extraction, Dimentionality Reduction are important. We also learn about the famous methods which can be used for the purpose. Data Preprocessing is a very important step in Data Analytics which is ignored by many. To make your models accurate you have to ensure proper preprocessing as the Machine Learning model is highly dependent on data. For all Ipython notebooks, used in this series : https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon Python for Data Analysis book : http://amzn.to/2oDief8 Pattern Recognition and Machine Learning : http://amzn.to/2p6mD6R
Views: 16872 The Semicolon
Text Classification - Natural Language Processing With Python and NLTK p.11
 
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Now that we understand some of the basics of of natural language processing with the Python NLTK module, we're ready to try out text classification. This is where we attempt to identify a body of text with some sort of label. To start, we're going to use some sort of binary label. Examples of this could be identifying text as spam or not, or, like what we'll be doing, positive sentiment or negative sentiment. Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 106949 sentdex
Introduction to Data Mining: Similarity & Dissimilarity
 
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In this Data Mining Fundamentals tutorial, we introduce you to similarity and dissimilarity. Similarity is a numerical measure of how alike two data objects are, and dissimilarity is a numerical measure of how different two data objects are. We also discuss similarity and dissimilarity for single attributes. -- Learn more about Data Science Dojo here: https://hubs.ly/H0hCsmV0 Watch the latest video tutorials here: https://hubs.ly/H0hCr-80 See what our past attendees are saying here: https://hubs.ly/H0hCsmW0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 20736 Data Science Dojo
Text Summarization - TensorFlow and Deep Learning Singapore
 
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Speaker: Anusha Sample Code: https://github.com/anooshac/machine-learning-projects/tree/master/text-summarizer Event Page: https://www.meetup.com/TensorFlow-and-Deep-Learning-Singapore/events/239252636/ Produced by Engineers.SG Help us caption & translate this video! http://amara.org/v/7PAC/
Views: 13915 Engineers.SG
Modern Text Summarization using Deep AI Networks
 
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This presentation is made at H2O Artificial Intelligence Meetup on 12 Sep 2017 on the topic of "The Magic of Text Summarization using Deep Networks". This presentation summarizes the approaches and techniques used to summarize text using neural networks. The relevant slides could be found at https://www.slideshare.net/SKReddy1/the-magic-of-text-summarization-using-deep-networks
Views: 3783 SK Reddy
Text Summarization in SpaCy and Python
 
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In this tutorial on Natural language processing we will be learning about Text/Document Summarization in Spacy. How to summarized a text or document with spacy and python in a simple way. Code For this Video: Github http://bit.ly/2RnNSf3 Check out This Course - Learn Julia Fundamentals http://bit.ly/2QLiLG8 ===Written Tuts== https://jcharistech.wordpress.com/2018/12/31/text-summarization-using-spacy-and-python/ If you liked the video don't forget to leave a like or subscribe. If you need any help just message me in the comments, you never know it might help someone else too. J-Secur1ty JCharisTech ==Get The Learn Julia App== @ Playstore : http://bit.ly/2NOiV2u @ Amazon :https://amzn.to/2OYOQdd Follow https://www.facebook.com/jcharistech/ https://github.com/Jcharis/ https://twitter.com/JCharisTech https://jcharistech.wordpress.com/
Views: 1901 J-Secur1ty
Naïve Bayes Classifier -  Fun and Easy Machine Learning
 
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Naive Bayes Classifier- Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS COURSE - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHINE LEARNING COURSES - http://augmentedstartups.info/machine-learning-courses -------------------------------------------------------------------------------- Now Naïve Bayes is based on Bayes Theorem also known as conditional Theorem, which you can think of it as an evidence theorem or trust theorem. So basically how much can you trust the evidence that is coming in, and it’s a formula that describes how much you should believe the evidence that you are being presented with. An example would be a dog barking in the middle of the night. If the dog always barks for no good reason, you would become desensitized to it and not go check if anything is wrong, this is known as false positives. However if the dog barks only whenever someone enters your premises, you’d be more likely to act on the alert and trust or rely on the evidence from the dog. So Bayes theorem is a mathematic formula for how much you should trust evidence. So lets take a look deeper at the formula, • We can start of with the Prior Probability which describes the degree to which we believe the model accurately describes reality based on all of our prior information, So how probable was our hypothesis before observing the evidence. • Here we have the likelihood which describes how well the model predicts the data. This is term over here is the normalizing constant, the constant that makes the posterior density integrate to one. Like we seen over here. • And finally the output that we want is the posterior probability which represents the degree to which we believe a given model accurately describes the situation given the available data and all of our prior information. So how probable is our hypothesis given the observed evidence. So with our example above. We can view the probability that we play golf given it is sunny = the probability that we play golf given a yes times the probability it being sunny divided by probability of a yes. This uses the golf example to explain Naive Bayes. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 166548 Augmented Startups
Text Classification Using Naive Bayes
 
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This is a low math introduction and tutorial to classifying text using Naive Bayes. One of the most seminal methods to do so.
Views: 101509 Francisco Iacobelli
Topic Modeling with Python
 
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Topic models are a suite of algorithms that uncover the hidden thematic structure in document collections. These algorithms help us develop new ways to search, browse and summarize large archives of texts. This talk will introduce topic modeling and one of it's most widely used algorithms called LDA (Latent Dirichlet Allocation). Attendees will learn how to use Python to analyze the content of their text documents. The talk will go through the full topic modeling pipeline: from different ways of tokenizing your document, to using the Python library gensim, to visualizing your results and understanding how to evaluate them.
Views: 46118 PyTexas
IOM 547: Designing Spreadsheet-Based Business Models-Gideon Weiss
 
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This course focuses on structuring, analyzing, and solving managerial decision problems across business areas. Using Excel as the modeling platform, we learn to summarize useful information from available data, optimally allocate limited resources, synthesize sequences of decisions, and incorporate uncertainties into analysis. The course covers plenty of practical examples and offers ample problem solving opportunities.
How do I select features for Machine Learning?
 
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Selecting the "best" features for your Machine Learning model will result in a better performing, easier to understand, and faster running model. But how do you know which features to select? In this video, I'll discuss 7 feature selection tactics used by the pros that you can apply to your own model. At the end, I'll give you my top 3 tips for effective feature selection. WANT TO JOIN MY NEXT WEBCAST? Become a member ($5/month): https://www.patreon.com/dataschool === RELATED RESOURCES === Dimensionality reduction presentation: https://www.youtube.com/watch?v=ioXKxulmwVQ Feature selection in scikit-learn: http://scikit-learn.org/stable/modules/feature_selection.html Sequential Feature Selector from mlxtend: http://rasbt.github.io/mlxtend/user_guide/feature_selection/SequentialFeatureSelector/ == WANT TO GET BETTER AT MACHINE LEARNING? == 1) WATCH my scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A 2) SUBSCRIBE for more videos: https://www.youtube.com/dataschool?sub_confirmation=1 3) ENROLL in my Machine Learning course: https://www.dataschool.io/learn/ 4) LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 16671 Data School
64 Cosine Similarity Example
 
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For Full Course Experience Please Go To http://mentorsnet.org/course_preview?course_id=1 Full Course Experience Includes 1. Access to course videos and exercises 2. View & manage your progress/pace 3. In-class projects and code reviews 4. Personal guidance from your Mentors
Views: 45135 Oresoft LWC
Data Cleaning Process Steps / Phases [Data Mining] Easiest Explanation Ever (Hindi)
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 24196 5 Minutes Engineering
Algorithmic Bias: From Discrimination Discovery to Fairness-Aware Data Mining (Part 1)
 
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Authors: Carlos Castillo, EURECAT, Technology Centre of Catalonia Francesco Bonchi, ISI Foundation Abstract: Algorithms and decision making based on Big Data have become pervasive in all aspects of our daily lives lives (offline and online), as they have become essential tools in personal finance, health care, hiring, housing, education, and policies. It is therefore of societal and ethical importance to ask whether these algorithms can be discriminative on grounds such as gender, ethnicity, or health status. It turns out that the answer is positive: for instance, recent studies in the context of online advertising show that ads for high-income jobs are presented to men much more often than to women [Datta et al., 2015]; and ads for arrest records are significantly more likely to show up on searches for distinctively black names [Sweeney, 2013]. This algorithmic bias exists even when there is no discrimination intention in the developer of the algorithm. Sometimes it may be inherent to the data sources used (software making decisions based on data can reflect, or even amplify, the results of historical discrimination), but even when the sensitive attributes have been suppressed from the input, a well trained machine learning algorithm may still discriminate on the basis of such sensitive attributes because of correlations existing in the data. These considerations call for the development of data mining systems which are discrimination-conscious by-design. This is a novel and challenging research area for the data mining community. The aim of this tutorial is to survey algorithmic bias, presenting its most common variants, with an emphasis on the algorithmic techniques and key ideas developed to derive efficient solutions. The tutorial covers two main complementary approaches: algorithms for discrimination discovery and discrimination prevention by means of fairness-aware data mining. We conclude by summarizing promising paths for future research. More on http://www.kdd.org/kdd2016/ KDD2016 conference is published on http://videolectures.net/
Views: 1776 KDD2016 video
Simple Deep Neural Networks for Text Classification
 
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Hi. In this video, we will apply neural networks for text. And let's first remember, what is text? You can think of it as a sequence of characters, words or anything else. And in this video, we will continue to think of text as a sequence of words or tokens. And let's remember how bag of words works. You have every word and forever distinct word that you have in your dataset, you have a feature column. And you actually effectively vectorizing each word with one-hot-encoded vector that is a huge vector of zeros that has only one non-zero value which is in the column corresponding to that particular word. So in this example, we have very, good, and movie, and all of them are vectorized independently. And in this setting, you actually for real world problems, you have like hundreds of thousands of columns. And how do we get to bag of words representation? You can actually see that we can sum up all those values, all those vectors, and we come up with a bag of words vectorization that now corresponds to very, good, movie. And so, it could be good to think about bag of words representation as a sum of sparse one-hot-encoded vectors corresponding to each particular word. Okay, let's move to neural network way. And opposite to the sparse way that we've seen in bag of words, in neural networks, we usually like dense representation. And that means that we can replace each word by a dense vector that is much shorter. It can have 300 values, and now it has any real valued items in those vectors. And an example of such vectors is word2vec embeddings, that are pretrained embeddings that are done in an unsupervised manner. And we will actually dive into details on word2vec in the next two weeks. But, all we have to know right now is that, word2vec vectors have a nice property. Words that have similar context in terms of neighboring words, they tend to have vectors that are collinear, that actually point to roughly the same direction. And that is a very nice property that we will further use. Okay, so, now we can replace each word with a dense vector of 300 real values. What do we do next? How can we come up with a feature descriptor for the whole text? Actually, we can use the same manner as we used for bag of words. We can just dig the sum of those vectors and we have a representation based on word2vec embeddings for the whole text, like very good movie. And, that's some of word2vec vectors actually works in practice. It can give you a great baseline descriptor, a baseline features for your classifier and that can actually work pretty well. Another approach is doing a neural network over these embeddings.
Views: 15927 Machine Learning TV
TN TRB Computer Science Syllabus - Business Computing #5 Data Mining
 
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Tamil Nadu TRB Computer Science Instructor GRADE1 Exam Syllabus Business Computing - Data Mining ------ Data Mining • It's the process to find patterns or relationship of Data using algorithms. • It's a process of analysing data from different perspectives and summarising it into useful information. • It gives answer which Data Base cannot give Data • Raw fact Eg: • Petrol price is Rs.75 per litter on 1st April • Petrol price is Rs.76.25 PL on 2nd April This data (price and date) stored in DB Information • We can get some information from Data Eg: • Price of the petrol is between Rs70 to Rs80 per litter in April Knowledge • From the useful Data and useful Information we can get some knowledge Eg: • When Petrol price is increasing by Rs5, the inflation rating is increased by 2% Data Mining: Extract the useful Decision / Answer from the Data Data • You can trust this Data, which is always correct based on current status that is stored in DB Information • The information gathered from Data is dynamic, which is getting changed based on Data • May be different for different time / place / person • Information is collected from Data. Data Mining Process / Life cycle 1. Data • Raw data from DB 2. Target Data • Split the necessary data • Selection of Data 3. Pre-processed Data • To remove unnecessary data • Deduct missing data 4. Transformed Data • Save the data in different form which can be mined • Normalized the data 5. Mining the Data • Extract the decisions by Patterns / Templates • By mathematical rules and algorithms • Also called machine-learning algorithms 6. Knowledge • Interpret the patterns to knowledge by user • From the Mined data template / pattern we can get the knowledge Extracting the knowledge from the Data is Data Mining EG of Machine-Learning algorithms • Classification learning • Numeric estimation • Association learning • and more Classification learning • Classify the characteristics of Objects/Entities • EG: A consumer will buy a new car in next year = Yes / No • Train the Machine using training data from the data what we have (Transformed data and Patterns) Appling the decision tree, showroom member can predict the new customer ability.
Cosine Similarity and IDF Modified Cosine Similarity
 
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This video tutorial explains the cosine similarity and IDF-Modified cosine similarity with very simple examples (related to Text-Mining/IR/NLP). It also demonstrates the Java implementation of cosine similarity. The source code can be downloaded from:- 1. Cosine similarity: https://sites.google.com/site/nirajatweb/home/technical_and_coding_stuff/cosine_similarity 2. IDF-Modified cosine similarity: https://sites.google.com/site/nirajatweb/home/technical_and_coding_stuff/idf_modified_cosine_similarity
Views: 9918 Dr. Niraj Kumar
Natural Language Processing with Graphs
 
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William Lyon, Developer Relations Enginner, Neo4j:During this webinar, we’ll provide an overview of graph databases, followed by a survey of the role for graph databases in natural language processing tasks, including: modeling text as a graph, mining word associations from a text corpus using a graph data model, and mining opinions from a corpus of product reviews. We'll conclude with a demonstration of how graphs can enable content recommendation based on keyword extraction.
Views: 33523 Neo4j
DEFCON 17: Dangerous Minds: The Art of Guerrilla Data Mining
 
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Speaker: Mark Ryan Del Moral Talabis Senior Consultant, Secure-DNA Consulting It is not a secret that in today's world, information is as valuable or maybe even more valuable that any security tool that we have out there. Information is the key. That is why the US Information Awareness Office's (IAO) motto is "scientia est potential", which means "knowledge is power". The IAO just like the CIA, FBI and others make information their business. Aside from these there are multiple military related projects like TALON,ECHELON, ADVISE, and MATRIX that are concerned with information gathering and analysis. The goal of the Veritas Project is to model itself in the same general threat intelligence premise as the organization above but primarily based on community sharing approach and using tools, technologies, and techniques that are freely available. Often, concepts that are part of artificial intelligence, data mining, and text mining are thought to be highly complex and difficult. Don't mistake me, these concepts are indeed difficult, but there are tools out there that would facilitate the use of these techniques without having to learn all the concepts and math behind these topics. And as sir Isaac Newton once said, "If I have seen further it is by standing on the shoulders of giants". The combination of all the techniques presented in this site is what we call "Guerrilla Data Mining". It's supposed to be fast, easy, and accessible to anyone. The techniques provides more emphasis on practicality than theory. For example, these tools and techniques presented can be used to visualize trends (e.g. security trends over time), summarize large and diverse data sets (forums, blogs, irc), find commonalities (e.g. profiles of computer criminals) gather a high level understanding of a topic (e.g. the US economy, military activities), and automatically categorize different topics to assist research (e.g. malware taxonomy). Aside from the framework and techniques themselves, the Veritas Project hopes to present a number of current ongoing studies that uses "guerilla data mining". Ultimately, our goal is to provide as much information in how each study was done so other people can generate their own studies and share them through the project. The following studies are currently available and will be presented: For more information visit: http://bit.ly/defcon17_information To download the video visit: http://bit.ly/defcon17_videos
Views: 3929 Christiaan008
Data Mining, Classification, Clustering, Association Rules, Regression, Deviation
 
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Complete set of Video Lessons and Notes available only at http://www.studyyaar.com/index.php/module/20-data-warehousing-and-mining Data Mining, Classification, Clustering, Association Rules, Sequential Pattern Discovery, Regression, Deviation http://www.studyyaar.com/index.php/module-video/watch/53-data-mining
Views: 91052 StudyYaar.com
Outlier Detection/Removal Algorithm
 
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This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 16011 Udacity
Final Year Projects | An Ontology-based Approach to Text Summarization
 
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Views: 809 Clickmyproject
Combining Data Owner side and Cloud side Access Control for Encrypted Cloud Storage
 
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Combining Data Owner side and Cloud side Access Control for Encrypted Cloud Storage- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project DATA MINING 1. Opinion Aspect Relations in Cognizing Customer Feelings via Reviews(24 January 2018) 2. Optimizing a multi-product continuous-review inventory model with uncertain demand, quality improvement, setup cost reduction, and variation control in lead time (27 June 2018) 3. Evaluation of Predictive Data Mining Algorithms in Soil Data Classification for Optimized Crop Recommendation (09 April 2018) 4. Prediction of Effective Rainfall and Crop Water Needs using Data Mining Techniques (01 February 2018) 5. A Secure Client-Side Framework for Protecting the Privacy of Health DataStored on the Cloud( 04 June 2018) 6. Greedy Optimization for K-Means-Based Consensus Clustering(April 2018) 7. A Two-stage Biomedical Event Trigger Detection Method Integrating Feature Selection and Word Embeddings 8. Principal Component Analysis Based Filtering for Scalable, High Precision k-NN Search 9. Entity Linking: A Problem to Extract Corresponding Entity with Knowledge Base 10. Collective List-Only Entity Linking: A Graph-Based Approach 11. Web Media and Stock Markets : A Survey and Future Directionsfrom a Big Data Perspective 12. Selective Database Projections Based Approach for Mining High-Utility Itemsets 13. Reverse k Nearest Neighbor Search over Trajectories 14. Range-based Nearest Neighbor Queries with Complex-shaped Obstacles 15. Predicting Contextual Informativeness for Vocabulary Learning 16. Online Product Quantization 17. Highlighter: automatic highlighting of electronic learning documents 18. Fuzzy Bag-of-Words Model for Document Representation 19. Frequent Itemsets Mining with Differential Privacy over Large-scale Data 20. Fast Cosine Similarity Search in Binary Space with Angular Multi-index Hashing 21. Efficient Vertical Mining of High Average-Utility Itemsets based on Novel Upper-Bounds 22. Document Summarization for Answering Non-Factoid Queries 23. Discovering Canonical Correlations between Topical andTopological Information in Document Networks 24. Complementary Aspect-based Opinion Mining 25. An Efficient Method for High Quality and Cohesive Topical Phrase Mining 26. A Weighted Frequent Itemset Mining Algorithm for Intelligent Decision in Smart Systems 27. A Correlation-based Feature Weighting Filter for Naive Bayes 28. Comments Mining With TF-IDF: The Inherent Bias and Its Removal 29. Bayesian Nonparametric Learning for Hierarchical and Sparse Topics 30. Supervised Topic Modeling using Hierarchical Dirichlet Process-based Inverse Regression: Experiments on E-Commerce Applications 31. Emotion Recognition on Twitter: Comparative Study and Training a Unison Model 32. Search Result Diversity Evaluation based on Intent Hierarchies 33. A Two-Phase Algorithm for Differentially Private Frequent Subgraph Mining 34. Automated Phrase Mining from Massive Text Corpora 35. Automatic Segmentation of Dynamic Network Sequences with Node Labels
Views: 12 Micans Infotech
Cheng Xiang Zhai - Latent Aspect Rating Analysis of Review Text Data
 
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With the rapid growth of online reviews on the Web, it is increasingly difficult for people to digest all the reviews about an entity such as a product or service, making it interesting to develop automated analysis techniques to reveal and summarize detailed opinions buried in large amounts of review text. In this talk, I will introduce a new way to analyze review text data called Latent Aspect Rating Analysis (LARA), which aims to perform three analysis tasks simultaneously: (1) identify the topical aspects of an entity discussed in reviews; (2) decompose the overall rating associated with a review into detailed ratings on each topical aspect; and (3) infer the relative emphasis on different aspects placed by a reviewer when forming the overall judgment of the entity. I will discuss two new generative statistical models for solving this problem. The first is a latent rating regression model, which solves the problem under the assumption that each topical aspect is pre-specified with a few keywords. The second is an extension of the latent regression model to incorporate a probabilistic topic model for further discovering the latent topical aspects, thus solving the whole LARA problem and performing all the three analysis tasks simultaneously with a unified model. I will present empirical evaluation results of these models on a hotel review data set to show that the proposed models can effectively solve the problem of LARA, and that the detailed analysis of opinions at the level of topical aspects enabled by the proposed model can support a wide range of application tasks, such as aspect opinion summarization, entity ranking based on aspect ratings, personalized entity recommendation, and analysis of rating behavior of reviewers.
Views: 1105 Rutgers CommInfo
Dynamic Clustering of Streaming Short Documents
 
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Author: Weinan Zhang, Department of Computer Science and Engineering, Shanghai Jiao Tong University Abstract: Clustering technology has found numerous applications in mining textual data. It was shown to enhance the performance of retrieval systems in various different ways, such as identifying different query aspects in search result diversification, improving smoothing in the context of language modeling, matching queries with documents in a latent topic space in ad-hoc retrieval, summarizing documents etc. The vast majority of clustering methods have been developed under the assumption of a static corpus of long (and hence textually rich) documents. Little attention has been given to streaming corpora of short text, which is the predominant type of data in Web 2.0 applications, such as social media, forums, and blogs. In this paper, we consider the problem of dynamically clustering a streaming corpus of short documents. The short length of documents makes the inference of the latent topic distribution challenging, while the temporal dynamics of streams allow topic distributions to change over time. To tackle these two challenges we propose a new dynamic clustering topic model - DCT - that enables tracking the time-varying distributions of topics over documents and words over topics. DCT models temporal dynamics by a short-term or long-term dependency model over sequential data, and overcomes the difficulty of handling short text by assigning a single topic to each short document and using the distributions inferred at a certain point in time as priors for the next inference, allowing the aggregation of information. At the same time, taking a Bayesian approach allows evidence obtained from new streaming documents to change the topic distribution. Our experimental results demonstrate that the proposed clustering algorithm outperforms state-of-the-art dynamic and non-dynamic clustering topic models in terms of perplexity and when integrated in a cluster-based query likelihood model it also outperforms state-of-the-art models in terms of retrieval quality. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 459 KDD2016 video
Optimizing a multi product continuous review inventory model with uncertain - IEEE PROJECTS 2018
 
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Optimizing a multi product continuous review inventory model with uncertain demand,quality improveme- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project NETWORK AND SERVICE MANAGEMENT 1. Bacterial foraging optimization based Radial Basis Function Neural Network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards Plant Pathology(12 February 2018 ) 2. Fault-Tolerant Clustering Topology Evolution Mechanism of Wireless Sensor Networks (08 June 2018) SOFTWARE ENGINEERING 1. Reviving Sequential Program Birthmarking for Multithreaded Software Plagiarism Detection 2. EVA: Visual Analytics to Identify Fraudulent Events DATA MINING 1. Opinion Aspect Relations in Cognizing Customer Feelings via Reviews(24 January 2018) 2. Optimizing a multi-product continuous-review inventory model with uncertain demand, quality improvement, setup cost reduction, and variation control in lead time (27 June 2018) 3. Evaluation of Predictive Data Mining Algorithms in Soil Data Classification for Optimized Crop Recommendation (09 April 2018) 4. Prediction of Effective Rainfall and Crop Water Needs using Data Mining Techniques (01 February 2018) 5. A Secure Client-Side Framework for Protecting the Privacy of Health DataStored on the Cloud( 04 June 2018) 6. Greedy Optimization for K-Means-Based Consensus Clustering(April 2018) 7. A Two-stage Biomedical Event Trigger Detection Method Integrating Feature Selection and Word Embeddings 8. Principal Component Analysis Based Filtering for Scalable, High Precision k-NN Search 9. Entity Linking: A Problem to Extract Corresponding Entity with Knowledge Base 10. Collective List-Only Entity Linking: A Graph-Based Approach 11. Web Media and Stock Markets : A Survey and Future Directionsfrom a Big Data Perspective 12. Selective Database Projections Based Approach for Mining High-Utility Itemsets 13. Reverse k Nearest Neighbor Search over Trajectories 14. Range-based Nearest Neighbor Queries with Complex-shaped Obstacles 15. Predicting Contextual Informativeness for Vocabulary Learning 16. Online Product Quantization 17. Highlighter: automatic highlighting of electronic learning documents 18. Fuzzy Bag-of-Words Model for Document Representation 19. Frequent Itemsets Mining with Differential Privacy over Large-scale Data 20. Fast Cosine Similarity Search in Binary Space with Angular Multi-index Hashing 21. Efficient Vertical Mining of High Average-Utility Itemsets based on Novel Upper-Bounds 22. Document Summarization for Answering Non-Factoid Queries 23. Discovering Canonical Correlations between Topical andTopological Information in Document Networks 24. Complementary Aspect-based Opinion Mining 25. An Efficient Method for High Quality and Cohesive Topical Phrase Mining 26. A Weighted Frequent Itemset Mining Algorithm for Intelligent Decision in Smart Systems 27. A Correlation-based Feature Weighting Filter for Naive Bayes 28. Comments Mining With TF-IDF: The Inherent Bias and Its Removal 29. Bayesian Nonparametric Learning for Hierarchical and Sparse Topics 30. Supervised Topic Modeling using Hierarchical Dirichlet Process-based Inverse Regression: Experiments on E-Commerce Applications
Actionable Mining of Large, Multi relational Data using Localized Predictive Models, Joydeep Ghosh
 
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Many large datasets associated with modern predictive data mining applications are quite complex and heterogeneous, possibly involving multiple relations, or exhibiting a dyadic nature with associated “side-information”. For example, one may be interested in predicting the preferences of a large set of customers for a variety of products, given various proper-ties of both customers and products, as well as past purchase history, a social network on the customers, and a conceptual hierarchy on the products. This talk will introduce a broad framework for effectively tackling such scenarios using a si-multaneous problem decomposition and modeling strategy that can exploit the wide variety of information available.
Views: 138 MMDS Foundation
Sentence Based Topic Modeling
 
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Data is not meaningful unless its information could be extracted. In every second in this world, we are generating millions of data over the internet in the different form. Most of them are in text format. Usually, data is written based on any topic, or sometimes on a few topics. Following this, identifying the topic of any text data is very important. Topic identification may help text summarization tools, text classification tool, etc. Machine learning applications may need less training on their data, only if once the topic of text is identified. Therefore, the demand for topic modeling is higher than ever right now. Data scientists are working day and night to make it more effective and accurate using different methods. Topic modeling focuses on the keywords that can express or identify the topic discussed in the document. Topic modeling can save a lot of time by releasing its user from page to page manual reviewing. In this paper, a model has been proposed to find out the topic of a document. This model works based on the relations between most frequent words and their relationship with the sentences in the document. This model can be used to increase the accuracy of the topic modeling.
Kalpa Gunaratna: Semantics-based Summarization of Entities in Knowledge Graphs
 
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Kalpa Gunaratna's Dissertation Defense: "Semantics-based Summarization of Entities in Knowledge Graphs" Wednesday, August 19, 2016 Advisors: Dr. Amit Sheth and Dr. Krishnaprasad Thirunarayan. Dissertation Committee: Dr. Keke Chen, Dr. Gong Cheng, Dr. Edward Curry, and Dr. Hamid R. Motahari-Nezhad. Homepage: http://knoesis.wright.edu/students/kalpa/ Pictures: https://www.facebook.com/media/set/?set=a.1499253150109528.1073741870.199004243467765&type=3 ABSTRACT: The processing of structured and semi-structured content on the Web has been gaining attention with the rapid progress in the Linking Open Data project and the development of commercial knowledge graphs. Knowledge graphs capture domain-specific or encyclopedic knowledge in the form of a data layer and add rich and explicit semantics on top the data layer to infer additional knowledge. The data layer of a knowledge graph represents entities and their descriptions. The semantic layer on top of the data layer is called the schema (ontology), where relationships of the entity descriptions, their classes, and the hierarchy of the relationships and classes are defined. Today, there exist large knowledge graphs in the research community (e.g., encyclopedic datasets like DBpedia and Yago) and corporate world (e.g., Google knowledge graph) that encapsulate a large amount of knowledge for human and machine consumption. Typically, they consist of millions of entities and billions of facts describing these entities. While it is good to have this much knowledge available on the Web for consumption, it leads to information overload, and hence proper summarization (and presentation) techniques need to be explored. In this dissertation, we focus on creating both comprehensive and concise entity summaries at: (i) the single entity level and (ii) the multiple entity level. To summarize a single entity, we propose a novel approach called FACeted Entity Summarization (FACES) that considers importance as well as the diversity of facts getting selected for the summary. We first conceptually group facts using semantic expansion and hierarchical incremental clustering techniques and form facets (i.e., groupings) that go beyond syntactic similarity. Then we rank both the facts and facets using Information Retrieval (IR) ranking techniques to pick the highest ranked facts in these facets for the summary. The important and unique contribution of this approach is that because of its generation of facets, it adds diversity into entity summaries, making them comprehensive. For creating multiple entity summaries, we simultaneously process facts belonging to the given entities using combinatorial optimization techniques. In this process, we maximize diversity and importance of facts within each entity summary and relatedness of facts between the entity summaries. The proposed approach uniquely combines semantic expansion, graph-based relatedness, and combinatorial optimization techniques to generate relatedness-based multi-entity summaries. Complementing the entity summarization approaches, we introduce a novel approach using light Natural Language Processing (NLP) techniques to enrich knowledge graphs by adding type semantics to literals. This makes datatype properties semantically rich compared to having only implementation types. As a result of the enrichment process, we could use both object and datatype properties in the entity summaries, which improves coverage in the entity summaries and can be useful in other applications like dataset profiling and data integration. We evaluate the proposed approaches against the state-of-the-art methods and highlight their capabilities for single and multiple entity summarization.
Views: 282 Knoesis Center
Data Preprocessing 2
 
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Project Name: e-Content generation and delivery management for student –Centric learning Project Investigator:Prof. D V L N Somayajulu
Views: 6043 Vidya-mitra
KDD2016 paper 1054
 
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Title: How to Compete Online for News Audience: Modeling Words that Attract Clicks Authors: Joon Hee Kim*, Korea Advanced Institute of Science and Technology Amin Mantrach, Yahoo! Research Alex Jaimes, Yahoo! Research Alice Oh, Korea Advanced Institute of Science and Technology Abstract: Headlines are particularly important for online news outlets where there are many similar news stories competing for users’ attention. Traditionally, journalists have followed rules-of-thumb and experience to master the art of crafting catchy headlines, but with the valuable resource of large-scale click-through data of online news articles, we can apply quantitative analysis and text mining techniques to acquire an in-depth understanding of headlines. In this paper, we conduct a large-scale analysis and modeling of 150K news articles published over a period of four months on the Yahoo home page. We define a simple method to measure click-value of individual words, and analyze how temporal trends and linguistic attributes affect click-through rate (CTR). We then propose a novel generative model, headline click-based topic model (HCTM), that extends latent Dirichlet allocation (LDA) to reveal the effect of topical context on the click-value of words in headlines. HCTM leverages clicks in aggregate on previously published headlines to identify words for headlines that will generate more clicks in the future. We show that by jointly taking topics and clicks into account we can detect changes in user interests within topics. We evaluate HCTM in two different experimental settings and compare its performance with ALDA (adapted LDA), LDA, and TextRank. The first task, full headline, is to retrieve full headline used for a news article given the body of news article. The second task, good headline, is to specifically identify words in the headline that have high click values for real users. For full headline task, our model performs on par with ALDA, a state-of-the art web-page summarization method that utilizes click-through information. For good headline task, which is of more practical importance to both individual journalists and online news outlets, our model significantly outperforms all other comparative methods. More on http://www.kdd.org/kdd2016/ KDD2016 Conference will be recorded and published on http://videolectures.net/
Views: 399 KDD2016 video
Identifying product opportunities using social media mining Application of topic modeling and chance
 
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Identifying product opportunities using social media mining Application of topic modeling and chance- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project DATA MINING 1. Opinion Aspect Relations in Cognizing Customer Feelings via Reviews(24 January 2018) 2. Optimizing a multi-product continuous-review inventory model with uncertain demand, quality improvement, setup cost reduction, and variation control in lead time (27 June 2018) 3. Evaluation of Predictive Data Mining Algorithms in Soil Data Classification for Optimized Crop Recommendation (09 April 2018) 4. Prediction of Effective Rainfall and Crop Water Needs using Data Mining Techniques (01 February 2018) 5. A Secure Client-Side Framework for Protecting the Privacy of Health DataStored on the Cloud( 04 June 2018) 6. Greedy Optimization for K-Means-Based Consensus Clustering(April 2018) 7. A Two-stage Biomedical Event Trigger Detection Method Integrating Feature Selection and Word Embeddings 8. Principal Component Analysis Based Filtering for Scalable, High Precision k-NN Search 9. Entity Linking: A Problem to Extract Corresponding Entity with Knowledge Base 10. Collective List-Only Entity Linking: A Graph-Based Approach 11. Web Media and Stock Markets : A Survey and Future Directionsfrom a Big Data Perspective 12. Selective Database Projections Based Approach for Mining High-Utility Itemsets 13. Reverse k Nearest Neighbor Search over Trajectories 14. Range-based Nearest Neighbor Queries with Complex-shaped Obstacles 15. Predicting Contextual Informativeness for Vocabulary Learning 16. Online Product Quantization 17. Highlighter: automatic highlighting of electronic learning documents 18. Fuzzy Bag-of-Words Model for Document Representation 19. Frequent Itemsets Mining with Differential Privacy over Large-scale Data 20. Fast Cosine Similarity Search in Binary Space with Angular Multi-index Hashing 21. Efficient Vertical Mining of High Average-Utility Itemsets based on Novel Upper-Bounds 22. Document Summarization for Answering Non-Factoid Queries 23. Discovering Canonical Correlations between Topical andTopological Information in Document Networks 24. Complementary Aspect-based Opinion Mining 25. An Efficient Method for High Quality and Cohesive Topical Phrase Mining 26. A Weighted Frequent Itemset Mining Algorithm for Intelligent Decision in Smart Systems 27. A Correlation-based Feature Weighting Filter for Naive Bayes 28. Comments Mining With TF-IDF: The Inherent Bias and Its Removal 29. Bayesian Nonparametric Learning for Hierarchical and Sparse Topics 30. Supervised Topic Modeling using Hierarchical Dirichlet Process-based Inverse Regression: Experiments on E-Commerce Applications 31. Emotion Recognition on Twitter: Comparative Study and Training a Unison Model 32. Search Result Diversity Evaluation based on Intent Hierarchies 33. A Two-Phase Algorithm for Differentially Private Frequent Subgraph Mining 34. Automated Phrase Mining from Massive Text Corpora 35. Automatic Segmentation of Dynamic Network Sequences with Node Labels
Views: 10 Micans Infotech
Jay Pujara: Better Knowledge Graphs Through Probabilistic Graphical Models
 
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Jay Pujara Title: Better Knowledge Graphs Through Probabilistic Graphical Models Abstract: Automated question answering, knowledgeable digital assistants, and grappling with the massive data flooding the Web all depend on structured knowledge. Precise knowledge graphs capturing the many, complex relationships between entities are the missing piece for many problems, but knowledge graph construction is notoriously difficult. In this talk, I will chronicle common failures from the first generation of information extraction systems and show how combining statistical NLP signals and semantic constraints addresses these problems. My method, Knowledge Graph Identification (KGI), exploits the key lessons of the statistical relational learning community and uses them for better knowledge graph construction. Probabilistic models are often discounted due to scalability concerns, but KGI translates the problem into a tractable convex objective that is amenable to parallelization. Furthermore, the inferences from KGI have provable optimality and can be updated efficiently using approximate techniques that have bounded regret. I demonstrate state-of-the-art performance of my approach on knowledge graph construction and entity resolution tasks on NELL and Freebase, and discuss exciting new directions for KG construction.
Views: 1274 AI2
Graph based Approach for Autamatic Text Summarization
 
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4th CSA Undergraduate Summer School 2016, Day 2 Session 3(c): By: B T Somaiah
Views: 1244 CSAChannel IISc
Prediction of effective rainfall and crop water needs using data mining techniques
 
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Prediction of effective rainfall and crop water needs using data mining techniques- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project SOFTWARE ENGINEERING,COMPUTER GRAPHICS 1. Reviving Sequential Program Birthmarking for Multithreaded Software Plagiarism Detection 2. EVA: Visual Analytics to Identify Fraudulent Events 3. Performance Specification and Evaluation with Unified Stochastic Probes and Fluid Analysis 4. Trustrace: Mining Software Repositories to Improve the Accuracy of Requirement Traceability Links 5. Amorphous Slicing of Extended Finite State Machines 6. Test Case-Aware Combinatorial Interaction Testing 7. Using Timed Automata for Modeling Distributed Systems with Clocks: Challenges and Solutions 8. EDZL Schedulability Analysis in Real-Time Multicore Scheduling 9. Ant Colony Optimization for Software Project Scheduling and Staffing with an Event-Based Scheduler 10. Locating Need-to-Externalize Constant Strings for Software Internationalization with Generalized String-Taint Analysis 11. Systematic Elaboration of Scalability Requirements through Goal-Obstacle Analysis 12. Centroidal Voronoi Tessellations- A New Approach to Random Testing 13. Ranking and Clustering Software Cost Estimation Models through a Multiple Comparisons Algorithm 14. Pair Programming and Software Defects--A Large, Industrial Case Study 15. Automated Behavioral Testing of Refactoring Engines 16. An Empirical Evaluation of Mutation Testing for Improving the Test Quality of Safety-Critical Software 17. Self-Management of Adaptable Component-Based Applications 18. Elaborating Requirements Using Model Checking and Inductive Learning 19. Resource Management for Complex, Dynamic Environments 20. Identifying and Summarizing Systematic Code Changes via Rule Inference 21. Generating Domain-Specific Visual Language Tools from Abstract Visual Specifications 22. Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers 23. On Fault Representativeness of Software Fault Injection 24. A Decentralized Self-Adaptation Mechanism for Service-Based Applications in the Cloud 25. Coverage Estimation in Model Checking with Bitstate Hashing 26. Synthesizing Modal Transition Systems from Triggered Scenarios 27. Using Dependency Structures for Prioritization of Functional Test Suites INFORMATION AND COMMUNICATION SYSTEM 1. A Data Mining based Model for Detection of Fraudulent Behaviour in Water Consumption SERVICES COMPUTING 1. SVM-DT-Based Adaptive and Collaborative Intrusion Detection (jan 2018) 2. Cloud Workflow Scheduling With Deadlines And Time Slot Availability (March-April 1 2018) 3. Secure and Sustainable Load Balancing of Edge Data Centers in Fog Computing (17 May 2018) 4. Semantic-based Compound Keyword Search over Encrypted Cloud Data 5. Quality and Profit Assured Trusted Cloud Federation Formation: Game Theory Based Approach 6. Optimizing Autonomic Resources for the Management of Large Service-Based Business Processes
What is a Data Science In English - Data Science demo by Balaji -vlr-9059868766  Machine Learning
 
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What is a Data Science In English - demo by Balaji -Vlr Training 905986876 Kukatpally - Hyderabad Venkat:9059868766 Jio :7013158918 For Data Science Course content http://www.sivaitsoft.com/data-science-online-training-kukatpally/ FaceBookPage: https://www.facebook.com/DataScience-Training-Kukatpally-829603420550121/ What is a Data Science Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.Data science - Wikipedia DATA SCIENCE (I)Introduction to Data Science and Python 1. Python Basics with Anaconda 2. Files and Loops 3. Booleans and If Statements 4. Files loops and Condition Logics with Application Example 5. List Operations, Dictionaries 6. Introduction to Functions 7. Debugging Errors 8. Project: Exploring US Date Births 9. Modules, Classes 10. Error Handling 11. List Comprehensions 12. Project: Modules, Classes, Error Handling, List Comprehensions by Using NFL Suspension Data 13. Variable Scopes 14. Regular Expressions 15. Dates in Python 16. Project: Exploring Gun Deaths in US (II) Data Analysis and Visualization 1. Getting Started with Numpy 2. Computation with Numpy 3. Introduction to Pandas 4. Data Manipulation with Pandas 5. Working with Missing Data 6. Project: Summarizing Data 7. Pandas Internal Series 8. Data Frames in Pandas 9. Project: Analyzing Thanks Giving Dinner 10. Project: Finding Patterns in Crime Exploratory Data Visualization 11. Line Charts 12. Multiple Plots 13. Bar Plots and Scatter Plots 14. Histograms and Box Plots 15. Project: Visualizing Earnings based on college Majors Story Telling Through Visualization 16. Improving Plot Aesthitics 17. Color Layout and Annotations 18. Project: Visualizing Gender Gaps in Colleges 19. Conditional Plots 20. Project:Visualizing Geographical Data (III) Data Cleaning 1. Data Cleaning Walkthrough 2. Data Cleaning Walkthrough Combining the data 3. Analyzing and Visualizing the Data 4. Project: Analyzing NYC High School Data 5. Project: Star Wars Survey (IV) Working with Data Sources 1. APIS and Web Scrapping (I) Working with APIS (II) Intermediate APIS (III) Working with REDDIT API (IV) Web Scrapping 2. SQL Fundamentals (I) Introduction to SQL (II) Summary Statistics (III) Group Summary Statistics (IV) Querying SQLITE from Python (V) Project: Analyzing CIA Facebook Data Using SQLITE and Python 3. SQL Intermediate (I) Modifying Data (II) Table Schemas (III) Database Normalization and Relations (IV) Postgre SQL and Installation 4. Advanced SQL (i) Indexing and Multicolumn Indexing (ii) Project: Analyzing Basketball data (V) Statistics and Probability 1. Introduction to Statistics 2. Standard Deviation and Correlation 3. Linear Regression 4. Distributions and Sampling 5. Project: Analyzing Movie Reviews 6. Introduction to Probability 7. Calculating Probabilities 8. Probability Distributions 9. Significance Testing 10. Chi Squared Test 11. Multi Category Chi Squared Test 12. Project: Wining Jeopardy (VI) Machine Learning 1. Machine Learning Fundamentals 2. Introduction to KNN 3. Evaluating Model Performances 4. Multivariate KNN 5. Hyper Parameter Optimization 6. Cross Validation 7. Project: Predicting Car Prices 8. Calculus for Machine Learning 9. Understanding Extreme points, limits and Linear & Nonlinear Functions 10. Linear Algebra (Linear Systems, Matrices, vectors, Solution Sets) 11. Linear Regression Model 12. Feature Selection 13. Gradient Descent 14. Ordinary Least Squares 15. Processing and Transforming Features 16. Project:Predicting House sales Prices 17. Logistic Regression 18. Evaluating Binary Classifiers 19. Multiclass Classification 20. Intermediate Linear Regression 21. Overfitting 22. Clustering Basics 23. K-Means Clustering 24. Gradient Descent 25. Introduction to Neural Networks 26. Project: Predicting the Stock Market 27. Introduction to Decision Trees 28. Building, Applying Decision Trees 29. Introduction to Random Forest 30. Project: Predicting Bike Rentals Machine Learning Projects 1. Data Cleaning 2. Preparing Features 3. Making Predictions 4. Sentiment Analysis (VII) Spark and Map Reduce 1. Introduction to Spark 2. Spark integration with Jupyter 3. Transformations and Actions 4. Spark Data Frames 5. Spark SQL (VIII) Building a Capstone Project ----------------------------------- data science tutorial
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How SVM (Support Vector Machine) algorithm works
 
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In this video I explain how SVM (Support Vector Machine) algorithm works to classify a linearly separable binary data set. The original presentation is available at http://prezi.com/jdtqiauncqww/?utm_campaign=share&utm_medium=copy&rc=ex0share
Views: 543914 Thales Sehn Körting
Auto Text Summarization
 
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PPT For Details Is Here: https://drive.google.com/file/d/0B3uT8Rls4MQUZUhiTjFEZGxQWUk/view?usp=sharing Website: www.projectwale.com (+919004670813)