# Package weka.clusterers

package weka.clusterers

Class
Description
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.
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.
DBSCAN
Basic implementation of DBSCAN clustering algorithm that should *not* be used as a reference for runtime benchmarks: more sophisticated implementations exist! Clustering of new instances is not supported.
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
OPTICS
Basic implementation of OPTICS clustering algorithm that should *not* be used as a reference for runtime benchmarks: more sophisticated implementations exist! Clustering of new instances is not supported.
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
SingleClustererEnhancer
Meta-clusterer for enhancing a base clusterer.
UpdateableClusterer
Interface to incremental cluster models that can learn using one instance at a time.
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.