Package weka.clusterers

Interface Summary

Interface	Description
Clusterer	Interface for clusterers.
DensityBasedClusterer	Interface for clusterers that can estimate the density for a given instance.
NumberOfClustersRequestable	Interface to a clusterer that can generate a requested number of clusters
UpdateableClusterer	Interface to incremental cluster models that can learn using one instance at a time.

Class Summary

Class	Description
AbstractClusterer	Abstract clusterer.
AbstractDensityBasedClusterer	Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.
Canopy	Cluster data using the capopy clustering algorithm, which requires just one pass over the data.
CheckClusterer	Class for examining the capabilities and finding problems with clusterers.
ClusterEvaluation	Class for evaluating clustering models.
Cobweb	Class implementing the Cobweb and Classit clustering algorithms. Note: the application of node operators (merging, splitting etc.) in terms of ordering and priority differs (and is somewhat ambiguous) between the original Cobweb and Classit papers.
EM	Simple EM (expectation maximisation) class. EM assigns a probability distribution to each instance which indicates the probability of it belonging to each of the clusters.
FarthestFirst	Cluster data using the FarthestFirst algorithm. For more information see: Hochbaum, Shmoys (1985).
FilteredClusterer	Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.
HierarchicalClusterer	Hierarchical clustering class.
MakeDensityBasedClusterer	Class for wrapping a Clusterer to make it return a distribution and density.
RandomizableClusterer	Abstract utility class for handling settings common to randomizable clusterers.
RandomizableDensityBasedClusterer	Abstract utility class for handling settings common to randomizable clusterers.
RandomizableSingleClustererEnhancer	Abstract utility class for handling settings common to randomizable clusterers.
SimpleKMeans	Cluster data using the k means algorithm.
SingleClustererEnhancer	Meta-clusterer for enhancing a base clusterer.