Package weka.clusterers

Class Summary

Clusterer	Abstract clusterer.
ClusterEvaluation	Class for evaluating clustering models.
Cobweb	Class implementing the Cobweb and Classit clustering algorithms.
DensityBasedClusterer	Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.
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:
MakeDensityBasedClusterer	Class for wrapping a Clusterer to make it return a distribution and density.
SimpleKMeans	Simple k means clustering class.