TRAINING data mining with weka
About Us AN INTRODUCTION TO DATA MINING WITH WEKA ( 14)
BY OPEN MINER
WWW.OPEN-MINER.COMPart 0
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INTRODUCTION TO DATA MINING WITH WEKA 14
Instructors . Japan Advance Institute of Science and Technology
Computer Engineering Computer Engineering Email:
[email protected], [email protected]
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INTRODUCTION TO DATA MINING WITH WEKA 14
Course Outline1st day Introduction to data mining Introduction
to Weka PreprocessRegression & Classification Techniques Linear
Regression Decision tree
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INTRODUCTION TO DATA MINING WITH WEKA 14
Course Outline (cont)2nd dayRegression & Classification
Techniques K-Nearest neighbors Neural Networks Support Vector
Machines (SVM)Clustering Association rule discovery JAVA + WEKA PHP
+ WEKA Knowledge Flow
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AN INTRODUCTION TO DATA MINING WITH WEKA 14
BY OPEN MINER
WWW.OPEN-MINER.COMPart IIntroduction to Data Mining
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INTRODUCTION TO DATA MINING WITH WEKA 14
What is data mining?The exploration and analysis of large
quantities of data in order to discover meaningful patterns and
rules Data Mining Techniques (2nd Edition) (patterns)
(relation)
Extraction of interesting (non-trivial, previously, unknown and
potential useful) information from data in large databases Data
Mining Concepts &Techniques (2nd Edition)
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INTRODUCTION TO DATA MINING WITH WEKA 14
Loyalty Cards
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INTRODUCTION TO DATA MINING WITH WEKA 14
Loyalty Cards (2)Reference : positioning 2552*OPEN
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Tesco LotusBigCCarrefourTOPSClub cardBigCardI
wishSPOT08/255209/255207/2550~2548 2 1 5 500 personal shopping
list
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INTRODUCTION TO DATA MINING WITH WEKA 14
Summary* Social network : facebook, twitter protein sequence,
gene
Data mining OPEN
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Cross Reference Industry Standard Process
for Data MiningPart IIAN INTRODUCTION TO DATA MINING WITH WEKA
14
BY OPEN MINER
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INTRODUCTION TO DATA MINING WITH WEKA 14
CRISP-DM*CRoss-Industry Standard Process for Data Mining
(CRISP-DM) DaimlerChrysler SPSS NCRWorkflow data mining 6
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INTRODUCTION TO DATA MINING WITH WEKA 14
Data Mining WorkflowBusiness Understanding + Data Understanding
+
Data Preparation
80% *
: http://open-miner.com/2009/11/03/introduction-datamining/OPEN
MINERwww.open-miner.comwww.facebook.com/openminer
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INTRODUCTION TO DATA MINING WITH WEKA 14
CRISP Example*( http://www.nectec.or.th/NTJ/No11/No11.php ).
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INTRODUCTION TO DATA MINING WITH WEKA 14
CRISP Example (5)Data Understanding
2535-2542 10,000 476,085 2 1 9
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Stu_codeSexAddressGPA37058063MaleBangkok2.337058167MaleSongkla3.2.
Stu_codeSub_codeGrade37058063C+37058063D.
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INTRODUCTION TO DATA MINING WITH WEKA 14
CRISP Example (6)* (Old) (Old) (New) (New)OPEN
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Stu_codeSexAddressGPA37058063MaleBangkok2.337058167MaleSongkla3.2.
Stu_codeSub_codeGrade37058063C+37058063D.
Stu_codeSexAddressGPA37058063MaleBangkokBAD37058167MaleSongklaGOOD.
Stu_codeSub_codeGrade37058063Medium37058063Low.
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INTRODUCTION TO DATA MINING WITH WEKA 14
Data Mining Concepts and TechniquesClassification
Regression
Clustering
Associate
Supervised learningUnsupervised learning*OPEN
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INTRODUCTION TO DATA MINING WITH WEKA 14
Data Mining SoftwareCommercial SoftwareSAS Enterprise Miner
DB2 Intelligent Miner
Microsoft SQL Server 2008
Open source software or FreewareWeka
RapidMiner
KNIME (Konstanz Information Miner)
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INTRODUCTION TO DATA MINING WITH WEKA 14
Data Mining Software (2)*WekaOPEN
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Introduction to WekaPart IIIAN INTRODUCTION TO DATA MINING WITH
WEKA 14
BY OPEN MINER
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INTRODUCTION TO DATA MINING WITH WEKA 14
What is Weka ?WekaWaikato Environment for Knowledge Analysis
open source Data Mining !!! Java WindowsLinuxMAC OS Weka
http://www.cs.waikato.ac.nz/ml/weka/
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INTRODUCTION TO DATA MINING WITH WEKA 14
Download Wekahttp://www.cs.waikato.ac.nz/ml/weka/
Java
Java Java !!*OPEN
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Weka Explorer*OPEN
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AN INTRODUCTION TO DATA MINING WITH WEKA 14
BY OPEN MINER
WWW.OPEN-MINER.COM PreprocessPart IV
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INTRODUCTION TO DATA MINING WITH WEKA 14
Agenda* (instance) (attribute)
Weka CSV ARFF
Preprocess Data Weka Outlier
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INTRODUCTION TO DATA MINING WITH WEKA 14
input WekaLoad data into Weka (2)*
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Lab 4-1: Generate CSV file *: CSV Excel Weka 1:
Note : In sex attribute, value 0 = Female, 1 = Male, 2 = Others
customers.csv
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Weka & MySQL (4)
URL : URL database serverClick User*OPEN
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Replace missing values in Weka
Choose filters unsupervised attribute ReplaceMissingValues
Apply
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Regression & ClassificationAN INTRODUCTION TO DATA MINING
WITH WEKA 14
BY OPEN MINER
WWW.OPEN-MINER.COMPart V
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INTRODUCTION TO DATA MINING WITH WEKA 14
Agenda* (Regression) (Data classification) training, testing
Linear RegressionDecision treeK-nearest neighborsNeural
NetworkSupport Vector Machines (SVM)
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What is classification?* : ?OPEN
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Example: Classification (3)*Model training data classDecision
tree model
Evaluate Model TreeOPEN
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INTRODUCTION TO DATA MINING WITH WEKA 14
Example: Classification (4)*Unseen data (class)
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134.8696.01158.83
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INTRODUCTION TO DATA MINING WITH WEKA 14
Classification Steps (3) *(Training data)(Evaluate data)
(classification model building)
(evaluation)
*Unseen data
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Classification in Weka (cont)* tab ClassifyOPEN
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1: Classifier
classification Bayes (probability) FunctionsLazy
classification
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INTRODUCTION TO DATA MINING WITH WEKA 14
Lab 5-2: German Credit Card *Business Understanding (decision
support system)
Data Understanding 600 GermanCreditBalance.arff CD
dataset/GermanCredit GermanCredit.pdf
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AN INTRODUCTION TO DATA MINING WITH WEKA 14
BY OPEN MINER
WWW.OPEN-MINER.COMPart VI Clustering
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INTRODUCTION TO DATA MINING WITH WEKA 14
Segmentation
(geographic) (demographic) (behavior)
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Data clustering (clustering) (cluster)
(unsupervised learning)
1
3 2
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Clustering in Weka (cont) tab Cluster
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Example1 : Clustering bank data (bank)*OPEN
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AN INTRODUCTION TO DATA MINING WITH WEKA 14
BY OPEN MINER
WWW.OPEN-MINER.COMAssociation RulesPart VII
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INTRODUCTION TO DATA MINING WITH WEKA 14
Market Basket Analysis supermarket ?
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EMBED Word.Picture.8
_973329925.unknown
_973329925.unknown
_1014102287.doc
_973329925.unknown
_1014102449.doc
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INTRODUCTION TO DATA MINING WITH WEKA 14
Data from point-of-sale (4)*POS database
Transaction databaseOPEN
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Transaction- timeProduct01-13-2009 20:04Apple01-13-2009
20:04Beer01-13-2009 20:04Cereal01-13-2009 20:04Diapers01-14-2009
11:30Apple01-14-2009 11:30Beer01-14-2009 11:30Diapers01-14-2009
11:30Eggs01-15-2009 14:15Beer01-15-2009 14:15Eggs
TIDProduct1Apple, Beer, Cereal, Diapers2Apple, Beer, Diapers,
Eggs3Beer, Eggs
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Association Rules in Weka (cont) tab Associate*OPEN
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Lab 7-1: Market Basket *Business Understanding CRM
Data Understanding 1,000 2
supermarket_basket_transactions_2005.arff
CD dataset/supermarketOPEN
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AN INTRODUCTION TO DATA MINING WITH WEKA 14
BY OPEN MINER
WWW.OPEN-MINER.COMPart VIII Command line & Integrate System
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Weka in command line Weka DOS Run > cmd DOS ()*OPEN
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Lab8-2: Weka in Java Program (2)* Weka Explorer
compile javac -classpath C:\Program Files\Weka-3-6\weka.jar
testClassifier.java run java -classpath C:\Program
Files\Weka-3-6\weka.jar;. testClassifierOPEN
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AN INTRODUCTION TO DATA MINING WITH WEKA 14
BY OPEN MINER
WWW.OPEN-MINER.COMPart IX Knowledge Flow
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INTRODUCTION TO DATA MINING WITH WEKA 14
Weka KnowledgeFlow component Weka workflow *
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Example: Knowledge flow (7)* component TextViewer Visualization
Layout ClassifierPerformanceEvaluator text
TextViewer
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Contact Us. [email protected]
Websitehttp://www.open-miner.com
Google Buzzhttp://www.google.com/profiles/openminer
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