1. Associate
1.1. Start
1.2. Choose
1.2.1. PredictiveApriori
1.2.2. Apriori
1.2.3. FilteredAssociator
1.2.4. FPGrowth
1.2.5. Tertius
1.2.6. GeneralizedSequentialPatterns
1.3. Stop
2. Cluster
2.1. Clusterer
2.1.1. CLOPE
2.1.2. Cobweb
2.1.3. DBSCAN
2.1.4. EM
2.1.5. FarthestFirst
2.1.6. FilteredClusterer
2.1.7. HierarchicalClusterer
2.1.8. MakeDensityBasedClusterer
2.1.9. OPTICS
2.1.10. SimpleKMeans
2.2. Cluster mode
2.2.1. Use training set
2.2.2. Supplied test set
2.2.3. Percentage split
2.2.4. Classes to clusters evaluation
3. Select Attributes
3.1. Attribute Selection Mode
3.1.1. Use full training set
3.1.1.1. Untitled
3.1.2. Cross-Validation
3.1.2.1. Seed
3.1.2.2. Folds
3.2. Attribute Evaluator
3.2.1. CfsSubsetEval
3.2.2. ChiSquaredAttributeEval
3.2.3. ClassifierSubsetEval
3.2.4. ConsistencySubsetEval
3.2.5. CostSensitiveAttributeEval
3.2.6. CostSensitiveSubsetEval
3.2.7. FilteredAttributeEval
3.2.8. FilteredSubsetEval
3.2.9. GainRatioAttributeEval
3.2.10. InfoGainAttributeEval
3.2.11. LatentSemanticAnalysis
3.2.12. OneRAttributeEval
3.2.13. PrincipalComponents
3.2.14. ReliefFAttributeEval
3.2.15. SVMAAttributeEval
3.2.16. SymmetiraclUncertAttributeEval
3.2.17. WrapperSubsetEval
3.3. Search Method
3.3.1. BestFirst
3.3.2. ExhaustiveSearch
3.3.3. GeneticSearch
3.3.4. GreedyStepwise
3.3.5. LinearForwardSelection
3.3.6. RaceSearch
3.3.7. RandomSearch
3.3.8. Ranker
3.3.9. RankSearch
3.3.10. ScatterSearchV1
3.3.11. SubsetSizeForwardSelection