# Package weka.clusterers

package weka.clusterers

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