# 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.