Uses of Class
weka.clusterers.AbstractClusterer

Packages that use AbstractClusterer

Package	Description
weka.clusterers

Uses of AbstractClusterer in weka.clusterers

Subclasses of AbstractClusterer in weka.clusterers

Modifier and Type	Class	Description
class	AbstractDensityBasedClusterer	Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.
class	Canopy	Cluster data using the capopy clustering algorithm, which requires just one pass over the data.
class	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.
class	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.
class	FarthestFirst	Cluster data using the FarthestFirst algorithm. For more information see: Hochbaum, Shmoys (1985).
class	FilteredClusterer	Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.
class	HierarchicalClusterer	Hierarchical clustering class.
class	MakeDensityBasedClusterer	Class for wrapping a Clusterer to make it return a distribution and density.
class	RandomizableClusterer	Abstract utility class for handling settings common to randomizable clusterers.
class	RandomizableDensityBasedClusterer	Abstract utility class for handling settings common to randomizable clusterers.
class	RandomizableSingleClustererEnhancer	Abstract utility class for handling settings common to randomizable clusterers.
class	SimpleKMeans	Cluster data using the k means algorithm.
class	SingleClustererEnhancer	Meta-clusterer for enhancing a base clusterer.