Weka(DataMining Tool)

Ajou University

Introduction to Weka

• Developed by University of Waikato(Keep being updated since 1999)

• Java based Open source application(Free to download)

• Uses for Datamining & Machine learning

Installation

• Download Link: http://www.cs.waikato.ac.nz/ml/weka/downloading.html

Installation

How to Start Weka

or

Options of Weka

Preprocess- open / edit / save data- modify data by preprocessing to use it

Classify- Select classifier to perform classification

or regression- Training & Testing the data

Cluster- Select clustering algorithm to make

clusters for the data

Associate- Analyze the data by using associator

to make association rules

Select Attribute- Select effective attributes

Visualize- Show 2D graph or plot of the data

Preprocessing

Useful & Most used Preprocessing Filters

- DiscretizeDiscretize a range of numeric attributes in the dataset into nominal attribute

- NormalizeNormalize(-1 ~ 1) all numeric values in the given dataset

- NumericToNominalTurning numeric attributes into nominal ones

- ReplaceMissingValuesReplacing all missing values for nominal and numeric attributes in dataset withthe modes and means from the training data

- StandardizeStandardizes all numeric attributes in the given dataset to have zero mean and unit variance

- StringToNominalTurning string attributes into nominal ones

- SwapValuesSwaps two values of a nominal attributes

Preprocessing

Example> Discretize Function(using “diabetes.arff”)

13

20

Discretized

Classification

Useful & Most used Classifier

- AttributeSelectedClassifierDimensionality of training and test data is reduced by attribute selectionbefore being passed on to a classifier

- ClassificationViaClustering(Regression)A simple meta-classifier that uses a clusterer(regression model) for classification

- DecisionTableClass for building and using a simple decision table majority classifier

- IBk(k-NN)K-nearest neighbor classifier

- LibSVMA wrapper class for using a LibSVM(Library for Support Vector Machine)

- NaiveBayesA Naïve Bayes classifier using estimator class

Classification

Example> IBk(k-NN) Classifier(using “KDDCup99_sample.arff”)

Classification

Example> IBk(k-NN) Classifier(using “KDDCup99_sample.arff”)

Classification

Example> IBk(k-NN) Classifier(using “KDDCup99_sample.arff”)

Classification

Saving, Loading, and Using of built Model

Classification

Saving, Loading, and Using of built Model

Clustering

Useful & Most used Clusterer

- EMUsing simple EM(Expectation Maximization) algorithm

- HierarchicalClustererUsing Agglomerative clustering algorithm

- SimpleKMeansUsing K-means clustering algorithm

Clustering

Example> SimpleKmean(using “KDDCup99_sample.arff”)

Clustering

Example> SimpleKmean(using “KDDCup99_sample.arff”)

Visualization

Select attributions

Example> CfsSubsetEval/BestFirst(using “KDDCup99_sample.arff”)

Select attributions

Example> CfsSubsetEval/BestFirst(using “KDDCup99_sample.arff”)

Association

Example> Apriori(using “KDDCup99_sample.arff”)

Association

Example> Apriori(using “KDDCup99_sample.arff”)