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6 Types of Classification Algorithms
 
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Here are some of the most commonly used classification algorithms -- Logistic Regression, Naïve Bayes, Stochastic Gradient Descent, K-Nearest Neighbours, Decision Tree, Random Forest and Support Vector Machine. https://analyticsindiamag.com/7-types-classification-algorithms/ -------------------------------------------------- Get in touch with us: Website: www.analyticsindiamag.com Contact: [email protected] Facebook: https://www.facebook.com/AnalyticsIndiaMagazine/ Twitter: http://www.twitter.com/analyticsindiam Linkedin: https://www.linkedin.com/company-beta/10283931/ Instagram: https://www.instagram.com/analyticsindiamagazine/
Topic Detection with Text Mining
 
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Meet the authors of the e-book “From Words To Wisdom”, right here in this webinar on Tuesday May 15, 2018 at 6pm CEST. Displaying words on a scatter plot and analyzing how they relate is just one of the many analytics tasks you can cover with text processing and text mining in KNIME Analytics Platform. We’ve prepared a small taste of what text mining can do for you. Step by step, we’ll build a workflow for topic detection, including text reading, text cleaning, stemming, and visualization, till topic detection. We’ll also cover other useful things you can do with text mining in KNIME. For example, did you know that you can access PDF files or even EPUB Kindle files? Or remove stop words from a dictionary list? That you can stem words in a variety of languages? Or build a word cloud of your preferred politician’s talk? Did you know that you can use Latent Dirichlet Allocation for automatic topic detection? Join us to find out more! Material for this webinar has been extracted from the e-book “From Words to Wisdom” by Vincenzo Tursi and Rosaria Silipo: https://www.knime.com/knimepress/from-words-to-wisdom At the end of the webinar, the authors will be available for a Q&A session. Please submit your questions in advance to: [email protected] This webinar only requires basic knowledge of KNIME Analytics Platform which you can get in chapter one of the KNIME E-Learning Course: https://www.knime.com/knime-introductory-course
Views: 3837 KNIMETV
How to Build a Text Mining, Machine Learning Document Classification System in R!
 
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We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 164901 Timothy DAuria
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: 96983 Francisco Iacobelli
Data Mining Lecture - - Finding frequent item sets | Apriori Algorithm | Solved Example (Eng-Hindi)
 
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In this video Apriori algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining in hindi, Finding frequent item sets, data mining, data mining algorithms in hindi, data mining lecture, data mining tools, data mining tutorial,
Views: 212428 Well Academy
Twitter Sentiment Analysis - Learn Python for Data Science #2
 
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In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is. The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More on TextBlob: https://textblob.readthedocs.io/en/dev/ Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html Best book to become a Python god: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e 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: 267022 Siraj Raval
Brian Lange | It's Not Magic: Explaining Classification Algorithms
 
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PyData Chicago 2016 As organizations increasingly make use of data and machine learning methods, people must build a basic "data literacy". Data scientist & instructor Brian Lange provides simple, visual & equation-free explanations for a variety of classification algorithms geared towards helping understand them. He shows how the concepts explained can be pulled off using Python library Scikit Learn in a few lines.
Views: 9510 PyData
Webinar: Sentiment Analysis: Deep Learning, Machine Learning, Lexicon Based?
 
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“Great movie with a nice story!” What do you think, did the person like the film or hate it? Most of the time it’s easy for us to decide whether the message of a text is positive or negative. But what if you wanted to automate the process of understanding the sentiment? For example, if you have a lot of customers leaving comments, or people publishing movie reviews, you will want to discern the sentiment and find out who is posting positive or negative messages. Sentiment analysis is an important piece of many data analytics use cases. Whether it processes customer feedback, movie reviews, or tweets, sentiment scores often contribute an important piece to describing the whole scenario. These are just some examples of a long list of use cases for sentiment analysis, which includes social media analysis, 360 degree customer views, customer intelligence, competitive analysis and many more. To avoid doing this manually, we apply sentiment analysis and teach an algorithm to understand text and extract the sentiment using Natural Language Processing. The slides for this webinar are available at https://www.slideshare.net/KNIMESlides/sentiment-analysis-with-knime-analytics-platform
Views: 1191 KNIMETV
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! 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: 450970 sentdex
Lecture2 - Text Classification - Part 1
 
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This talk, named "Text Classification", was delivered by Prof. Pabitra Mitra at "Text Mining Workshop - 2014", organized by ACM Student Chapter, ISI Kolkata. This is part 1 of the lecture.
15 Hot Trending PHD Research Topics in Data Mining 2018
 
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15 Hot Trending Data Mining Research Topics 2018 1. Medical Data Mining 2. Education Data Mining 3. Data Mining with Cloud Computing 4. Efficiency of Data Mining Algorithms 5. Signal Processing 6. Social Media Analytics 7. Data Mining in Medical Science 8. Government Domain 9. Financial Data Analysis 10. Financial Accounting Fraud Detection 11. Customer Analysis 12. Financial Growth Analysis using Data Mining 13. Data Mining and IOT 14. Data Mining for Counter-Terrorism Key Research Application Fields: • Crisp-DM • Oracle Data mining • Web Mining • Open NN • Data Warehousing • Text Mining WHY YOU NEED TO OUTSOURCE TO PhD Assistance: a) Unlimited revisions b) 24/7 Admin Support c) Plagiarism Generate d) Best Possible Turnaround time e) Access to High qualified technical coordinators and expertise f) Support: Skype, Live Chat, Phone, Email Contact us: India: +91 8754446690 UK: +44-1143520021 Email: [email protected] Visit Webpage: https://goo.gl/HwJgqQ Visit Website: http://www.phdassistance.com
Views: 4791 PhD Assistance
Introduction to Snorkel by Stephen Bach
 
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Snorkel (https://hazyresearch.github.io/snorkel/) is a tool that automatically extracts information from unstructured data sources, such as the scientific literature and clinical notes, without using large, labeled training datasets, which are often lacking in biomedicine. In this workshop, participants learned about the Snorkel workflow through brief lectures and hands-on activities. This included: Writing labeling functions using pattern-matching and comparisons against existing dictionaries (e.g., Unified Medical Language System) Fitting and assessing a model to the labeling functions to generate the training data Hearing about examples of problems that can and cannot be addressed with Snorkel
Views: 1425 Mobilize Center
Text mining online data with scikit-learn by Robert Layton
 
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Text mining has a large variety of applications and is becoming used in more businesses for gathering intelligence and providing insight. People are sending text constantly online via social media, chat rooms and blogs. Tapping into this information can help businesses gain an advantage and is increasingly a necessary skill for data analytics. Text mining is a unique data mining problem, dealing with real world data that is often heavy on artefacts, difficult to model and challenging to properly manage. Text mining can be seen as a bit of a dark art that is difficult to learn and gain traction. However some basic strategies can often be applied to get good results quite quickly, and the same basic models appear in many text mining challenges. The scikit-learn project is a library of machine learning algorithms for the scientific python stack (numpy & scipy). It is known for having detailed documentation, a high quality of coding and a growing list of users worldwide. The documentation includes tutorials for learning machine learning as well as the library and is a great place to start for beginners wanting to learn data analytics. There is a strong focus on reusable components and useful algorithms, and the text mining sections of scikit-learn follow the “standard model” of text mining quite well. In this presentation, we will go through the scikit-learn project for machine learning and show how to use it for text mining applications. Real world data and applications will be used, including spam detection on Twitter, predicting the author of a program and determining a user's political bent based on their social media account. PyCon Australia is the national conference for users of the Python Programming Language. In August 2014, we're heading to Brisbane to bring together students, enthusiasts, and professionals with a love of Python from around Australia, and all around the World. August 1-5, Brisbane, Queensland, Australia
Views: 4287 PyCon Australia
Twitter Sentiment Analysis in Python using Tweepy and TextBlob
 
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In this tutorial we will do sentiment analysis in python by analyzing tweets about any topic happening in the world to see how positive or negative it's emotion is. We will use tweepy for fetching tweets and textblob for natural language processing (nlp) Text Based Tutorial http://www.letscodepro.com/Twitter-Sentiment-Analysis/ Github link for project https://github.com/the-javapocalypse/Twitter-Sentiment-Analysis Further Reading Material http://docs.tweepy.org/en/v3.5.0/api.html http://textblob.readthedocs.io/en/dev/ Please Subscribe! And like. And comment. That's what keeps me going. Follow Me Facebook: https://www.facebook.com/javapocalypse Instagram: https://www.instagram.com/javapocalypse
Views: 29189 Javapocalypse
Machine Learning: Ranking
 
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Ranking algorithms
Views: 9993 Jordan Boyd-Graber
Build a Text Summarizer in Java
 
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Get the Code here : https://github.com/ajhalthor/text-summarizer Follow me on Twitter : https://twitter.com/ajhalthor Take a look at the original by Shlomi Babluki : http://thetokenizer.com/2013/04/28/build-your-own-summary-tool/ TRANSCRIPT OVERVIEW ALGORITHM 1. Take the full CONTENT and split it into PARAGRAPHS. 2. Split each PARAGRAPH into SENTENCES. 3. Compare every sentence with every other. This is done by Counting the number of common words and then Normalize this by dividing by average number of words per sentence. 4. These intermediate scores/values are stored in an INTERSECTION matrix 5. Create the key-value dictionary - Key : Sentence - Value : Sum of intersection values with this sentence 6. From every paragraph, extract the sentences with the highest score. 7. Sort the selected sentences in order of appearance in the original text to preserve content and meaning. And like that, you have generated a summary of the original text. CLASSES IN JAVA PROJECT 1. Sentence : The entire text is divided into a number of paragraphs and each paragraph is divided into a number of sentences. 2. Paragraph : Every paragraph has a number associated with it and an Array List of sentences. 3. Sentence Comparitor : Compare Sentence objects based on Score 4. SentenceComparatorForSummary : Compare Sentence objects based on position in text. 5. SummayTool : akes care of all the operations from extracting sentences to generating the summary. HOW IS MY SUMMARIZER BETTER THAN THE ORIGINAL ? My text summarizer selects number of sentences from a paragraph depending on the length. This is an improvement over the original text summarizer implementation that only selects 1 sentence per paragraph regardless of length. So, If the author decides to crunch everything into 1 paragraph, then only one sentence would be chosen. In the current implementation, we set it to accept several sentences for larger paragraphs. It delivers cogent summaries for general essays, reviews and publications. RUN THIS PROGRAM $ javac -d bin improved_summary.java $ java -classpath bin improved_summary
Views: 7610 CodeEmporium
Weka Text Classification for First Time & Beginner Users
 
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59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 136895 Brandon Weinberg
Data Mining - Clustering
 
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What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 67007 deltaDNA
K mean clustering algorithm with solve example
 
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#kmean datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 356034 Last moment tuitions
Web Mining - Tutorial
 
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Web Mining Web Mining is the use of Data mining techniques to automatically discover and extract information from World Wide Web. There are 3 areas of web Mining Web content Mining. Web usage Mining Web structure Mining. Web content Mining Web content Mining is the process of extracting useful information from content of web document.it may consists of text images,audio,video or structured record such as list & tables. screen scaper,Mozenda,Automation Anywhere,Web content Extractor, Web info extractor are the tools used to extract essential information that one needs. Web Usage Mining Web usage Mining is the process of identifying browsing patterns by analysing the users Navigational behaviour. Techniques for discovery & pattern analysis are two types. They are Pattern Analysis Tool. Pattern Discovery Tool. Data pre processing,Path Analysis,Grouping,filtering,Statistical Analysis, Association Rules,Clustering,Sequential Pattterns,classification are the Analysis done to analyse the patterns. Web structure Mining Web structure Mining is a tool, used to extract patterns from hyperlinks in the web. Web structure Mining is also called link Mining. HITS & PAGE RANK Algorithm are the Popular Web structure Mining Algorithm. By applying Web content mining,web structure Mining & Web usage Mining knowledge is extracted from web data.
Anomaly Detection: Algorithms, Explanations, Applications
 
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Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 14843 Microsoft Research
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: 156093 Siraj Raval
5.1: Intro to Week 5: Text Analysis and Word Counting - Programming with Text
 
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Week 5 of Programming from A to Z focuses on about text-analysis and word counting. In this introduction, I discuss different how word counting and text analysis can be used in a creative coding context. I give an overview of the topics I will cover in this series of videos. Next Video: https://youtu.be/_5jdE6RKxVk http://shiffman.net/a2z/text-analysis/ Course url: http://shiffman.net/a2z/ Support this channel on Patreon: https://patreon.com/codingtrain Send me your questions and coding challenges!: https://github.com/CodingTrain/Rainbow-Topics Contact: https://twitter.com/shiffman GitHub Repo with all the info for Programming from A to Z: https://github.com/shiffman/A2Z-F16 Links discussed in this video: Rune Madsen's Programming Design Systems: http://printingcode.runemadsen.com/ Concordance on Wikipedia: https://en.wikipedia.org/wiki/Concordance_(publishing) Rune Madsen's Speech Comparison: https://runemadsen.com/work/speech-comparison/ Sarah Groff Hennigh-Palermo's Book Book: http://www.sarahgp.com/projects/book-book.html Stephanie Posavec: http://www.stefanieposavec.co.uk/ James W. Pennebaker's The Secret Life of Pronouns: http://www.secretlifeofpronouns.com/ James W. Pennebaker's TedTalk: https://youtu.be/PGsQwAu3PzU ITP from Tisch School of the Arts: https://tisch.nyu.edu/itp Source Code for the all Video Lessons: https://github.com/CodingTrain/Rainbow-Code p5.js: https://p5js.org/ Processing: https://processing.org For More Programming from A to Z videos: https://www.youtube.com/user/shiffman/playlists?shelf_id=11&view=50&sort=dd For More Coding Challenges: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6ZiZxtDDRCi6uhfTH4FilpH Help us caption & translate this video! http://amara.org/v/WuMg/
Views: 17296 The Coding Train
Last Minute Tutorials | Apriori algorithm | Association Rule Mining
 
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Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 78488 Last Minute Tutorials
Supervised and Unsupervised Machine Learning Algorithms - Machine Learning Tutorials In Hindi #6
 
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Text Tutorial + Source Code - http://mycodingzone.net/videos/hindi/machine-learning-hindi-6 This video is a part of the following Machine Learning Playlist - https://www.youtube.com/playlist?list=PL47S5PRS_XOej8y-tst51IY9J6tcOmrKg
What is a HashTable Data Structure - Introduction to Hash Tables , Part 0
 
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This tutorial is an introduction to hash tables. A hash table is a data structure that is used to implement an associative array. This video explains some of the basic concepts regarding hash tables, and also discusses one method (chaining) that can be used to avoid collisions. Wan't to learn C++? I highly recommend this book http://amzn.to/1PftaSt Donate http://bit.ly/17vCDFx STILL NEED MORE HELP? Connect one-on-one with a Programming Tutor. Click the link below: https://trk.justanswer.com/aff_c?offer_id=2&aff_id=8012&url_id=238 :)
Views: 788120 Paul Programming
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: 102452 sentdex
PageRank Algorithm - Example
 
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Full Numerical Methods Course: http://bit.ly/numerical-methods-java FREE Beginner Java Course: http://bit.ly/2rMkyxN
Views: 67737 Balazs Holczer
Machine Learning with Text in scikit-learn (PyCon 2016)
 
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Although numeric data is easy to work with in Python, most knowledge created by humans is actually raw, unstructured text. By learning how to transform text into data that is usable by machine learning models, you drastically increase the amount of data that your models can learn from. In this tutorial, we'll build and evaluate predictive models from real-world text using scikit-learn. (Presented at PyCon on May 28, 2016.) GitHub repository: https://github.com/justmarkham/pycon-2016-tutorial Enroll in my online course: http://www.dataschool.io/learn/ == OTHER RESOURCES == My scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A My pandas video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y == 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/ YouTube: https://www.youtube.com/user/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool
Views: 86564 Data School
NLP : Auto correction with Text in Python
 
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The application of computational techniques to the analysis and synthesis of natural language and speech. Python Core ------------ Video in English https://goo.gl/df7GXL Video in Tamil https://goo.gl/LT4zEw Python Web application ---------------------- Videos in Tamil https://goo.gl/rRjs59 Videos in English https://goo.gl/spkvfv Python NLP ----------- Videos in Tamil https://goo.gl/LL4ija Videos in English https://goo.gl/TsMVfT Artificial intelligence and ML ------------------------------ Videos in Tamil https://goo.gl/VNcxUW Videos in English https://goo.gl/EiUB4P ChatBot -------- Videos in Tamil https://goo.gl/JU2WPk Videos in English https://goo.gl/KUZ7PY YouTube channel link www.youtube.com/atozknowledgevideos Website http://atozknowledge.com/ Technology in Tamil & English
Views: 2798 atoz knowledge
K-Means Clustering - The Math of Intelligence (Week 3)
 
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Let's detect the intruder trying to break into our security system using a very popular ML technique called K-Means Clustering! This is an example of learning from data that has no labels (unsupervised) and we'll use some concepts that we've already learned about like computing the Euclidean distance and a loss function to do this. Code for this video: https://github.com/llSourcell/k_means_clustering Please Subscribe! And like. And comment. That's what keeps me going. More learning resources: http://www.kdnuggets.com/2016/12/datascience-introduction-k-means-clustering-tutorial.html http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.html http://people.revoledu.com/kardi/tutorial/kMean/ https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html http://mnemstudio.org/clustering-k-means-example-1.htm https://www.dezyre.com/data-science-in-r-programming-tutorial/k-means-clustering-techniques-tutorial http://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html 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: 97087 Siraj Raval
Deep Learning Approach for Extreme Multi-label Text Classification
 
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Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modeling to document tagging in NLP, face recognition to learning universal feature representations in computer vision, gene function prediction in bioinformatics, etc. Extreme classification has also opened up a new paradigm for ranking and recommendation by reformulating them as multi-label learning tasks where each item to be ranked or recommended is treated as a separate label. Such reformulations have led to significant gains over traditional collaborative filtering and content-based recommendation techniques. Consequently, extreme classifiers have been deployed in many real-world applications in industry. This workshop aims to bring together researchers interested in these areas to encourage discussion and improve upon the state-of-the-art in extreme classification. In particular, we aim to bring together researchers from the natural language processing, computer vision and core machine learning communities to foster interaction and collaboration. Find more talks at https://www.youtube.com/playlist?list=PLD7HFcN7LXReN-0-YQeIeZf0jMG176HTa
Views: 9697 Microsoft Research
Machine Learning Tutorial 26 - ID3 Algorithm Part 2
 
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Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics). Machine Learning and Predictive Analytics. #MachineLearning Welcome to the second video of the ID3 Algorithm. Today we are talking about base cases! This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Support me! http://www.patreon.com/calebcurry Subscribe to my newsletter: http://bit.ly/JoinCCNewsletter Donate!: http://bit.ly/DonateCTVM2. ~~~~~~~~~~~~~~~Additional Links~~~~~~~~~~~~~~~ More content: http://CalebCurry.com Facebook: http://www.facebook.com/CalebTheVideoMaker Google+: https://plus.google.com/+CalebTheVideoMaker2 Twitter: http://twitter.com/calebCurry Amazing Web Hosting - http://bit.ly/ccbluehost (The best web hosting for a cheap price!)
Views: 1490 Caleb Curry
KDD ( knowledge data discovery )  in data mining in hindi
 
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#kdd #datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 72124 Last moment tuitions
Data Mining  High impact Strategies   What You Need to Know  Definitions  Adoptions  Impact  Benefit
 
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https://store.theartofservice.com/data-mining-high-impact-strategies-what-you-need-to-know-definitions-adoptions-impact-benefits-maturity-vendors.html In easy to read chapters, with extensive references and links to get you to know all there is to know about Data Mining right away, covering: Data mining, Able Danger, Accuracy paradox, Affinity analysis, Alpha algorithm, Anomaly detection, Apatar, Apriori algorithm, Association rule learning, Automatic distillation of structure, Ball tree, Biclustering, Big data, Biomedical text mining, Business analytics, CANape, Cluster analysis, Clustering high-dimensional data, Co-occurrence networks, Concept drift, Concept mining, Consensus clustering, Correlation clustering, Cross Industry Standard Process for Data Mining, Cyber spying, Data Applied, Data classification (business intelligence), Data dredging, Data fusion, Data mining agent, Data Mining and Knowledge Discovery, Data mining in agriculture, Data mining in meteorology, Data stream mining, Data visualisation, DataRush Technology, Decision tree learning, Deep Web Technologies, Document classification, Dynamic item set counting, Early stopping, Educational data mining, Elastic map, Environment for DeveLoping KDD-Applications Supported by Index-Structures, Evolutionary data mining, Extension neural network, Feature Selection Toolbox, FLAME clustering, Formal concept analysis, General Architecture for Text Engineering, Group method of data handling, GSP Algorithm, In-database processing, Inference attack, Information Harvesting, Institute of Analytics Professionals of Australia, K-optimal pattern discovery, Keel (software), KXEN Inc., Languageware, Lattice Miner, Lift (data mining), List of machine learning algorithms, Local outlier factor, Molecule mining, Nearest neighbour search, Neural network, Non-linear iterative partial least squares, Open source intelligence, Optimal matching, Overfitting, Principal component analysis, Profiling practices, RapidMiner, Reactive Business Intelligence, Receiver operating characteristic, Ren-rou, Sequence mining, Silhouette (clustering), Software mining, Structure mining, Talx, Text corpus, Text mining, Transaction (data mining), Weather data mining, Web mining, Weka (machine learning), Zementis Inc.
Views: 159 TheArtofService
R Algorithms | Data Science With R Tutorial
 
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Here are some methods for organizing data, e.g. hashing, trees, queues, lists, priority queues. Streaming algorithms for computing statistics on the data. Sorting and searching. Basic graph models and algorithms for searching, shortest paths, and matching. Dynamic programming. Linear and convex programming. Floating point arithmetic, stability of numerical algorithms, Eigenvalues, singular values, PCA, gradient descent, stochastic gradient descent, and block coordinate descent. Conjugate gradient, Newton and quasi-Newton methods. Large scale applications from signal processing, collaborative filtering, recommendations systems, etc. Data Science Certification Training (R, SAS & Excel): http://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=R-algorithms-nhhTHZCs9v4&utm_medium=SC&utm_source=youtube For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 3751 Simplilearn
Node.js Mining and Displaying Twitter Sentiment Data
 
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Sample Node.js mining and displaying data example. https://github.com/kirkins/Twitter-Sentiment-Collector https://graniteapps.co
Views: 1064 Philip Kirkbride
Natural Language Processing in Artificial Intelligence in Hindi | NLP Easy Explanation
 
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Hello Friends Welcome to Well Academy In this video i am Explaining Natural Language Processing in Artificial Intelligence in Hindi and Natural Language Processing in Artificial Intelligence is explained using an Practical Example which will be very easy for you to understand. Artificial Intelligence lectures or you can say tutorials are explained by Abdul Sattar Another Channel Link for Interesting Videos : https://www.youtube.com/channel/UCnKlI8bIoRdgzrPUNvxqflQ Google Duplex video : https://www.youtube.com/watch?v=RPOAz48uEc0 Sample Notes Link : https://goo.gl/KY9g2e For Full Notes Contact us through Whatsapp : +91-7016189342 Form For Artificial Intelligence Topics Request : https://goo.gl/forms/suL3639o2TG8aKkG3 Artificial Intelligence Full Playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fug7z_YlD9M0x8gdZ7ziXen DBMS Gate Lectures Full Course FREE Playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fs6JyD7FFw9Ou1u601tev2D Computer Network GATE Lectures FREE playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fsO-ui9r_pmuDC3d2Oh9wWy Facebook Me : https://goo.gl/2zQDpD Click here to subscribe well Academy https://www.youtube.com/wellacademy1 GATE Lectures by Well Academy Facebook Group https://www.facebook.com/groups/1392049960910003/ Thank you for watching share with your friends Follow on : Facebook page : https://www.facebook.com/wellacademy/ Instagram page : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 59680 Well Academy
Graph Mining with Deep Learning - Ana Paula Appel (IBM)
 
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Talk Slides: https://drive.google.com/open?id=1nm3jU2sjLxoatWTenffraN3a6xt0QEE8 Deep learning is widely use in several cases with a good match and accuracy, as for example images classifications. But when to come to social networks there is a lot of problems involved, for example how do we represent a network in a neural network without lost node correspondence? Which is the best encode for graphs or is it task dependent? Here I will review the state of art and present the success and fails in the area and which are the perspective. Ana Paula is a Research Staff Member in IBM Research - Brazil, currently work with large amount of data to do Science WITH Data and Science OF Data at IBM Research Brazil. My technical interesting are in data mining and machine learning area specially in graph mining techniques for health and finance data. I am engage in STEAM initiatives to help girls and women to go to math/computer/science are. She is also passion for innovation and thus I become a master inventor at IBM.
Views: 294 PAPIs.io
How Search Works
 
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https://www.google.com/search/howsearchworks | The life span of a Google query is less then 1/2 second, and involves quite a few steps before you see the most relevant results. Here's how it all works.
Views: 4797753 Google
Movie Success Prediction Using Data Mining Project
 
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Get the project at http://nevonprojects.com/movie-success-prediction-using-data-mining/ The system predicts the success of a movie by mining past movie success data through a prediction methodology and data mining algorithms
Views: 20516 Nevon Projects
Orchestrating the Intelligent Web with Apache Mahout
 
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Presenter(s): Aneesha Bakharia URL: http://2011.linux.conf.au/programme/schedule/view_talk/213 Presenters: Aneesha Bakharia ([email protected]) and Aaron Tan ([email protected]) It is becoming increasingly important to incorporate “collective intelligence” within web, mobile and business intelligence applications. Traditionally the implementation of algorithms capable of adding intelligence to an application either required a highly specialised knowledge of machine learning or was extremely costly. Apache Mahout is one of the first open source and scalable machine learning libraries that seeks to mainstream the use of machine learning. This presentation will focus on providing the audience with a practical understanding of the algorithms included in Apache Mahout and how they can be used to provide insight into the patterns that exist in large amounts of data? Text clustering with the Latent Dirichlet Algorithm will also be covered. The Apache Mahout library consists of scalable machine learning algorithms for data mining tasks that encompass classification (Naïve Bayes and Support Vector Machines), clustering (k­means, Expectation Maximization, Mean Shift, Latent Dirichlet Allocation and Hierarchical Clustering), recommendation (collaborative filtering) and frequent pattern mining (parallel fp-growth). As of the 0.3 release, an impressive total of 25 machine learning algorithms have been implemented. Apache Mahout achieves scalability by leveraging Apache Hadoop which implements the MapReduce parallel processing paradigm that was first made popular by Google. Latent Dirichlet Allocation is a relatively new algorithm first introduced in 2003 with a suggested use in Topic Modeling (text clustering). Unlike generic clustering algorithms such as k-means, Latent Dirichlet Allocation is able to model document overlap. Latent Dirichlet Allocation is not a hard clustering algorithm and is able to map documents and words to multiple clusters. This feature is a natural fit for documents, which usually discuss multiple topics. The Latent Dirichlet Allocation algorithm simultaneously groups both documents and words into clusters. This is a useful feature as the main words belonging to a cluster and the prominent documents within a cluster are both output by the algorithm. Twitter recently released a feature called Lists that allows you to group people you follow and view the timeline of Tweets for users in a List separately. We will use the Latent Dirichlet Allocation algorithm to cluster people you follow and suggest Lists for Twitter. This will serve as a practical overview of using Apache Mahout for clustering. The following topics of interest are: - What is machine learning? - What is Apache Mahout? - Who is using Apache Mahout? - The MapReduce paradigm - Machine learning with Apache Mahout - Clustering with Apache Mahout - Classification with Apache Mahout - Collaborative Filtering with Apache Mahout - Frequent Pattern Mining with Apache Mahout - Processing Large Datasets with Multiple Cluster Nodes - Building a Twitter List recommendation application with the Latent Dirichlet Allocation algorithm http://2011.linux.conf.au/ - http://www.linux.org.au CC BY-SA - http://creativecommons.org/licenses/by-sa/4.0/legalcode.txt
Top Ten Machine Learning Algorithms | The Bad, The good, The Better data
 
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Provides an overview of top 10 machine learning algorithms for beginners and discussion about data quality. Becoming Data Scientist: https://goo.gl/JWyyQc Introductory R Videos: https://goo.gl/NZ55SJ Machine Learning videos: https://goo.gl/WHHqWP Deep Learning with TensorFlow: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi Text mining: https://goo.gl/7FJGmd Data Visualization: https://goo.gl/Q7Q2A8 Playlist: https://goo.gl/iwbhnE
Views: 1582 Bharatendra Rai
Rosette Dedupe for Rapidminer
 
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The Rosette Deduplicate Names operator identifies candidate duplicates from a list of names by assigning “group ids” to groups of matching names. The operator can process lists of up to 10,000 English names and assigns group ids based on a user-specified match threshold. The threshold sets the minimum similarity score required for two names to be considered duplicates. Thresholds can be set by clicking on the operator and entering a value between 0 and 1 in the “Threshold” field. We recommend starting with a .8 threshold, and experimenting with higher or lower values depending upon your use case and results. Given a list of names as input, the output is a list of cluster IDs (integers) for each name—not in any particular order. The output may then be sorted by cluster ID to group together possible duplicate names. https://www.rosette.com/blog/deduplicate-names-rapidminer-rosette/
Views: 432 Basis Technology
Apriori Algorithm in data mining | Cloud Technologies | IEEE Projects Hyderabad | Ameerpet
 
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Apriori Algorithm in data mining Website: - http://cloudstechnologies.in Like us on FB https://www.facebook.com/cloudtechnologiespro?ref=hl Follow us on https://twitter.com/cloudtechpro Cloud technologies is one of the best renowned software development company In Hyderabad India. We guide and train the students based on their qualification under the guidance of vast experienced real time developers.
Views: 70 Cloud Technologies
International Journal of Data Mining & Knowledge Management Process (IJDKP)
 
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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] . Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 179 ijdkp jou