Uses of Class
weka.clusterers.AbstractClusterer

Packages that use AbstractClusterer

weka.clusterers

Uses of AbstractClusterer in weka.clusterers

Subclasses of AbstractClusterer in weka.clusterers

class	AbstractDensityBasedClusterer Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.
class	CLOPE Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data.
class	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.
class	DBScan Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.
class	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.
class	FarthestFirst Cluster data using the FarthestFirst algorithm. For more information see: Hochbaum, Shmoys (1985).
class	FilteredClusterer Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.
class	HierarchicalClusterer Hierarchical clustering class.
class	MakeDensityBasedClusterer Class for wrapping a Clusterer to make it return a distribution and density.
class	OPTICS Mihael Ankerst, Markus M.
class	RandomizableClusterer Abstract utility class for handling settings common to randomizable clusterers.
class	RandomizableDensityBasedClusterer Abstract utility class for handling settings common to randomizable clusterers.
class	RandomizableSingleClustererEnhancer Abstract utility class for handling settings common to randomizable clusterers.
class	sIB Cluster data using the sequential information bottleneck algorithm. Note: only hard clustering scheme is supported.
class	SimpleKMeans Cluster data using the k means algorithm Valid options are:
class	SingleClustererEnhancer Meta-clusterer for enhancing a base clusterer.
class	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.