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Interface Summary | |
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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 | |
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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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