| AbstractClusterer |
Abstract clusterer.
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| AbstractDensityBasedClusterer |
Abstract clustering model that produces (for each test instance)
an estimate of the membership in each cluster
(ie.
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| Canopy |
Cluster data using the capopy clustering algorithm, which requires just one pass over the data.
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| CheckClusterer |
Class for examining the capabilities and finding problems with clusterers.
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| ClusterEvaluation |
Class for evaluating clustering models.
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| 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.
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| 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.
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| FarthestFirst |
Cluster data using the FarthestFirst algorithm.
For more information see:
Hochbaum, Shmoys (1985).
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| FilteredClusterer |
Class for running an arbitrary clusterer on data
that has been passed through an arbitrary filter.
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| HierarchicalClusterer |
Hierarchical clustering class.
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| MakeDensityBasedClusterer |
Class for wrapping a Clusterer to make it return a
distribution and density.
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| RandomizableClusterer |
Abstract utility class for handling settings common to randomizable
clusterers.
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| RandomizableDensityBasedClusterer |
Abstract utility class for handling settings common to randomizable
clusterers.
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| RandomizableSingleClustererEnhancer |
Abstract utility class for handling settings common to randomizable
clusterers.
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| SimpleKMeans |
Cluster data using the k means algorithm.
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| SingleClustererEnhancer |
Meta-clusterer for enhancing a base clusterer.
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