Search results “Tanagra data mining tool ppt presentation”
Tanagra Data Mining
an "open source project" as every researcher can access to the source code, and add his own algorithms, as far as he agrees and conforms to the software distribution license.
Views: 16343 Emmanuel Felipe
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Weka Data Mining Tutorial for First Time & Beginner Users
23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 471248 Brandon Weinberg
KEEL Data mining tool demo
KEEL Data minig tool Demo of installation and Working
Views: 4227 Manukumar K J
Tanagra - rudarenje podaka, sppi, D.P.
upoznavanje s alatom Tanagra - rudarenje podaka, projekni zadatak iz SKLADIŠTA PODATAKA I POSLOVNA INTELIGENCIJA
Views: 513 Dado FOI
What is Data Mining || Urdu/Hindi
We are the best web and mobile development organization in Germany that is inspired by cause to transform the thoughts into the reality. We build up the sites and portable applications that make the regularly enduring impressions and life-changing experiences. How about transforming the ideas into the greatest developments? Let's do it together. Comprehensive List of tools for Data Mining: 1- Rapid Miner 2- Weka 3- Orange 4- R 5- Knime 6- Rattle 7- Tanagra 8- XL Miner
Views: 146 MS Technologies
Data Mining with Weka (3.4: Decision trees)
Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 4: Decision trees http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/1LRgAI https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 73027 WekaMOOC
The CMSR Data Suite
A brief discussion on the CMSR Data Suite Music: Dreams by Joakim Karud https://soundcloud.com/joakimkarud Creative Commons — Attribution-ShareAlike 3.0 Unported— CC BY-SA 3.0 http://creativecommons.org/licenses/b... Music provided by Audio Library https://youtu.be/VF9_dCo6JT4
Views: 70 thepersonwho
More Data Mining with Weka (3.3: Association rules)
More Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 3: Association rules http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/nK6fTv https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 15968 WekaMOOC
How to Pronounce Tanagra
Learn how to say words in English correctly with Emma Saying free pronunciation tutorials. Over 140,000 words were already uploaded... Check them out! Visit my homepage: http://www.emmasaying.com
Views: 503 Emma Saying
Weka Text Classification for First Time & Beginner Users
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: 139335 Brandon Weinberg
NaiveBayes example
Simple example of the Naive Bayes classification algorithm
Views: 135150 Francisco Iacobelli
Using Data Analytics to Improve Medical Diagnosis (Schumacher/Robinson)
Diseases Associated with Pediatric IBD: Data Mining the HCUP KID Database with Association Analysis and Text Mining. By Linda Schumacher & Eric Robinson (Oklahoma State University)
Views: 296 Eric Robinson
Technical Course: Cluster Analysis: K-Means Algorithm for Clustering
K-Means Algorithm for clustering by Gaurav Vohra, founder of Jigsaw Academy. This is a clip from the Clustering module of our course on analytics. Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training Follow us on: https://www.facebook.com/jigsawacademy https://twitter.com/jigsawacademy http://jigsawacademy.com/
Views: 205019 Jigsaw Academy