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## Package weka.clusterers

Interface Summary
NumberOfClustersRequestable | Interface to a clusterer that can generate a requested number of clusters

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.

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