Package weka.clusterers
package weka.clusterers
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ClassDescriptionAbstract clusterer.Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.Class for examining the capabilities and finding problems with clusterers.Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data.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.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.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 clustersBasic 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.Abstract 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 sequential information bottleneck algorithm.
Note: only hard clustering scheme is supported.Cluster data using the k means algorithmMeta-clusterer for enhancing a base clusterer.Interface to incremental cluster models that can learn using one instance at a time.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.