weka.clusterers

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## Package weka.clusterers

Interface Summary
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
AbstractClusterer | Abstract clusterer.
AbstractDensityBasedClusterer | Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.
CheckClusterer | Class for examining the capabilities and finding problems with clusterers.
CLOPE | Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data.
ClusterEvaluation | Class for evaluating clustering models. Valid options are: -t name of the training file Specify the training file.
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.
DBScan | Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.
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.
MakeDensityBasedClusterer | Class for wrapping a Clusterer to make it return a distribution and density.
OPTICS | Mihael Ankerst, Markus M.
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.
sIB | Cluster data using the sequential information bottleneck algorithm. Note: only hard clustering scheme is supported.
SimpleKMeans | Cluster data using the k means algorithm Valid options are:
SingleClustererEnhancer | Meta-clusterer for enhancing a base clusterer.
XMeans | Cluster data using the X-means algorithm. X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region.

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