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Evaluation of WEKA. Waikato Environment for Knowledge Analysis Presented By: Manoj Wartikar & Sameer Sagade. Outline. Introduction to the WEKA System. Features Pros and Cons Enhancements. Introduction. A research project at the University of Waikato, NZ

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evaluation of weka
Evaluation of WEKA

Waikato Environment for Knowledge Analysis

Presented By:

Manoj Wartikar &

Sameer Sagade

outline
Outline
  • Introduction to the WEKA System.
  • Features
  • Pros and Cons
  • Enhancements
introduction
Introduction
  • A research project at the University of Waikato, NZ
  • Weka is a collection of machine learning algorithms for solving real-world data mining problems.
  • Developed in Java 2
features
Features
  • Documented features of WEKA
    • Attribute Selection
    • Clustering
    • Classification
    • Association Rules
    • Filters
    • Estimators
attribute selection
Attribute Selection
  • A part of the Preprocessing phase in the Knowledge Discovery process.
  • Useful to specify the attributes and their values on which data can be mined.
attribute selection contd
Attribute Selection contd….
  • Algorithms Implemented
    • Best First
    • Forward Selection
    • Ranked Output First
clustering
Clustering
  • Algorithms Implemented
    • Cobweb
    • Estimation Maximization
    • Clusterer
    • Distribution Clusterer
classification
Classification
  • Algorithms Implemented
    • K Nearest Neighbor
    • Naïve Bayes
    • Bagging
    • Boosting
    • Multi - Class Classifier
association rules
Association Rules
  • Algorithms Implemented
    • Apriori
filters
Filters
  • Algorithms Implemented
    • Attribute Filter
    • Discretize Filter
    • Split Dataset Filter
estimators
Estimators
  • Algorithms Implemented
    • Discrete Estimator
    • Kernel Estimator
    • Normal Estimator
    • Poisson Estimator
sample execution
Sample Execution

java weka.associations.Apriori -t data/weather.nominal.arff -I yes

Apriori

=======

Minimum support: 0.2

Minimum confidence: 0.9

Number of cycles performed: 17

Generated sets of large itemsets:

Size of set of large itemsets L(1): 12

sample execution1
Sample Execution

Best rules found:

1. humidity=normal windy=FALSE 4 ==> play=yes 4 (1)

2. temperature=cool 4 ==> humidity=normal 4 (1)

3. outlook=overcast 4 ==> play=yes 4 (1)

4. temperature=cool play=yes 3 ==> humidity=normal 3 (1)

5. outlook=rainy windy=FALSE 3 ==> play=yes 3 (1)

6. outlook=rainy play=yes 3 ==> windy=FALSE 3 (1)

7. outlook=sunny humidity=high 3 ==> play=no 3 (1)

8. outlook=sunny play=no 3 ==> humidity=high 3 (1)

boosting
Boosting
  • ADA Boost
  • Logit Boost
  • Decision Stump
pros and cons of weka
Pros and Cons of WEKA
  • Covers the Entire Machine Learning Process
  • Easy to compare the results of the different algorithms implemented
  • Accepts one of the most widely used data formats as input i.e the ARFF format.
pros and cons for weka1
Pros and Cons for WEKA
  • Textual User Interface
  • Requires the Java Virtual Machine to be installed for execution
  • Visualization of the mining results not possible
enhancements
Enhancements
  • The new version of WEKA 3.1.7 overcomes some of the decripancies of the previous version like
    • Graphical User Interface
    • Visualization of Results.
    • Mining of Non - local data bases