1. WEKA Evaluation of WEKA Waikato Environment for Knowledge Analysis Presented By: Manoj Wartikar & Sameer Sagade

2. WEKA Outline Introduction to the WEKA System. Features Pros and Cons Enhancements

3. WEKA 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

4. WEKA Features Documented features of WEKA –Attribute Selection –Clustering –Classification –Association Rules –Filters –Estimators

5. WEKA 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.

6. WEKA Attribute Selection contd…. Algorithms Implemented – Best First – Forward Selection – Ranked Output First

7. WEKA Clustering Algorithms Implemented – Cobweb – Estimation Maximization – Clusterer – Distribution Clusterer

8. WEKA Classification Algorithms Implemented – K Nearest Neighbor – Naïve Bayes – Bagging – Boosting – Multi - Class Classifier

9. WEKA Association Rules Algorithms Implemented – Apriori

10. WEKA Filters Algorithms Implemented – Attribute Filter – Discretize Filter – Split Dataset Filter

11. WEKA Estimators Algorithms Implemented – Discrete Estimator – Kernel Estimator – Normal Estimator – Poisson Estimator

12. WEKA 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

13. WEKA 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)

14. WEKA Boosting ADA Boost Logit Boost Decision Stump

15. 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.

16. WEKA Pros and Cons for WEKA Flexible APIs for programmers Customization possible

17. WEKA 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

18. WEKA 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