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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CheckClusterer |
Class for examining the capabilities and finding problems with
clusterers.
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CLOPE |
Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data.
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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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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.
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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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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.
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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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sIB |
Cluster data using the sequential information bottleneck algorithm.
Note: only hard clustering scheme is supported.
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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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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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