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Tanagra Data Mining
 
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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: 14307 Emmanuel Felipe
Tanagra - rudarenje podaka, sppi, D.P.
 
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upoznavanje s alatom Tanagra - rudarenje podaka, projekni zadatak iz SKLADIŠTA PODATAKA I POSLOVNA INTELIGENCIJA
Views: 429 Dado FOI
tanagra 03 cluster trans 01
 
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Views: 6150 xx3d2ybnf
data mining fp growth | data mining fp growth algorithm | data mining fp tree example | fp growth
 
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In this video FP growth 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 algorithms in hindi, data mining in hindi, data mining lecture, data mining tools, data mining tutorial, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining fp growth, data mining fp growth algorithm, data mining fp tree example, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining, fp growth algorithm, fp growth algorithm example, fp growth algorithm in data mining, fp growth algorithm in data mining example, fp growth algorithm in data mining examples ppt, fp growth algorithm in data mining in hindi, fp growth algorithm in r, fp growth english, fp growth example, fp growth example in data mining, fp growth frequent itemset, fp growth in data mining, fp growth step by step, fp growth tree
Views: 103054 Well Academy
KEEL Data mining tool demo
 
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KEEL Data minig tool Demo of installation and Working
Views: 3680 Manukumar K J
Weka Data Mining Tutorial for First Time & Beginner Users
 
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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: 430196 Brandon Weinberg
How to Pronounce Tanagra
 
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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: 456 Emma Saying
Demo Program Tugas Akhir Budiluhur - 1311510414
 
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Video Demo Program Tugas Akhir Judul : IMPLEMENTASI ALGORITMA KRIPTOGRAFI PADA APLIKASI PENGAMANAN EMAIL DENGAN METODE RIVEST CODE 4 (RC4) DAN DATA ENCRYPTION STANDARD (DES) BERBASIS MOBILE ANDROID PADA PT. TIRTA ABADI GEMILANG NIM : 1311510414 Nama : Diki Firmansyah Universitas Budi Luhur
Views: 93 Diki Firmansyah
Weka Tutorial 06: Discretization (Data Preprocessing)
 
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An important feature of Weka is Discretization where you group your feature values into a defined set of interval values. Experiments showed that algorithms like Naive Bayes works well with discretized feature values
Views: 55918 Rushdi Shams
The CMSR Data Suite
 
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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: 62 thepersonwho
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: 477806 Thales Sehn Körting
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: 131764 Brandon Weinberg
PRESENTASI DATA MINING  CLUSTERING PERGERAKAN DOLAR TERHADAP RUPIAH MENGGUNAKAN ALGORITMA K-MEANS
 
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KELOMPOK 3 NINA YULIANI RUDI HERMAWAN MUHAMMAD GHALLY
Views: 86 Nina Yuliani
NaiveBayes example
 
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Simple example of the Naive Bayes classification algorithm
Views: 122263 Francisco Iacobelli
Using Data Analytics to Improve Medical Diagnosis (Schumacher/Robinson)
 
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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: 276 Eric Robinson
Visualize the patent landscape of ice cream scoops with STN® AnaVist™
 
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An ice cream scoop helps provide us with rounded scoops of delicious ice cream but what else can the ice cream scoop do? Join us and find out what companies are looking for new applications and what are the latest research trends. With STN AnaVist, a powerful interactive analysis and visualization tool, we can visualize the patent landscape of the ice cream scoop and find new discoveries for a traditional application.
Views: 994 CAS
Technical Course: Cluster Analysis: K-Means Algorithm for Clustering
 
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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: 200115 Jigsaw Academy
Background mining
 
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Upresentation is the biggest base of PowerPoint templates on the Internet. Thousands of free templates are waiting for you. To download this template in PowerPoint format (.pptx) click the link below: http://www.upresentation.com/powerpoint-templates/background-mining/0000011190/
Views: 4 Roy Leet
Loading Data Into R Software - (read.table, Data/CSV Import Tutorial)
 
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Basic instructions on importing data into R statistics software for people just starting with R. You'll load a .csv file, tab-delineated text file, and a space-separated file. Download Data from this video: http://sites.google.com/site/curtiskephart/ta/econ113/NHIS_2007_data.csv More Econometrics and R Software Resources: https://sites.google.com/site/curtiskephart/ta/econ113 Download and install R: http://www.google.com/search?hl=en&q=Download+R+Software&btnI=745 Please send me questions. "Load data into r" finally ----------------------------------------
Views: 168205 economicurtis
How to Use SPSS: Discriminant Analysis
 
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Using multiple numeric predictor variables to predict a single categorical outcome variable.

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