Package weka.clusterers

Interface Summary
ActiveLearningClusterer  
SemiSupClusterer  
 

Class Summary
AlgVector Class for performing operations on an algebraic vector of floating-point values.
Cluster  
Clusterer Abstract clusterer.
ClusterEvaluation Class for evaluating clustering models.
Cobweb Class implementing the Cobweb and Classit clustering algorithms.
DistributionClusterer Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.
DistributionMetaClusterer Class for wrapping a Clusterer to make it return a distribution.
EM Simple EM (expectation maximisation) class.
FarthestFirst Implements the "Farthest First Traversal Algorithm" by Hochbaum and Shmoys 1985: A best possible heuristic for the k-center problem, Mathematics of Operations Research, 10(2):180-184, as cited by Sanjoy Dasgupta "performance guarantees for hierarchical clustering", colt 2002, sydney works as a fast simple approximate clusterer modelled after SimpleKMeans, might be a useful initializer for it Valid options are:
HAC  
InstancePair Class for handling a pair of instances, in terms of indices of instances in an Instances set
MPCKMeans Pairwise constrained k means clustering class.
PCKMeans Pairwise constrained k means clustering class.
PCSoftKMeans Pairwise constrained k means clustering class.
SeededKMeans Seeded k means clustering class.
Seeder  
SemiSupClustererEvaluation Class for evaluating clustering models - extends ClusterEvaluation.java
SimpleKMeans Simple k means clustering class.
XMeans XMeans clustering class.