Package weka.clusterers

Interface Summary
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
CheckClusterer Class for examining the capabilities and finding problems with clusterers.
Clusterer Abstract clusterer.
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.
DensityBasedClusterer Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.
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.
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.