WekaUT: Extensions to WEKA

• Clusterers package:

  • SemiSupClusterer: Interface for semi-supervised clustering
  • SeededEM, SeededKMeans: Implements SemiSupClusterer, has seeding
  • HAC, MatrixHAC: Implements top-down agglomerative clustering
  • ConsensusClusterer: Abstract class for consensus clustering
  • ConsensusPairwiseClusterer: Takes output of many clusterings, uses cluster collocation statistics as similarity values, applies clustering algo
  • CoTrainableClusterer: Performs co-trainable clustering, similar to Nigam’s Co-EM
  • CVEvaluation: 10-fold cross-validation with learning curves, in transductive framework

Previous slide

Next slide

Back to first slide

View graphic version