ClassDescriptionAbstract clusterer.Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.Cluster data using the capopy clustering algorithm, which requires just one pass over the data.Class for examining the capabilities and finding problems with clusterers.Interface for clusterers.Class for evaluating clustering models.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.Interface for clusterers that can estimate the density for a given instance.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.Cluster data using the FarthestFirst algorithm.
For more information see:
Hochbaum, Shmoys (1985).Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.Hierarchical clustering class.Class for wrapping a Clusterer to make it return a distribution and density.Interface to a clusterer that can generate a requested number of clustersAbstract utility class for handling settings common to randomizable clusterers.Abstract utility class for handling settings common to randomizable clusterers.Abstract utility class for handling settings common to randomizable clusterers.Cluster data using the k means algorithm.Meta-clusterer for enhancing a base clusterer.Interface to incremental cluster models that can learn using one instance at a time.