| AbstractKMeans |
Abstract base class for k-means implementations.
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| AbstractKMeans.Instance |
Inner instance for a run, for better encapsulation, that encapsulates the
standard flow of most (but not all) k-means variations.
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| AbstractKMeans.Par |
Parameterization class.
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| AnnulusKMeans |
Annulus k-means algorithm.
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| BestOfMultipleKMeans |
Run K-Means multiple times, and keep the best run.
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| BetulaLloydKMeans |
BIRCH/BETULA-based clustering algorithm that simply treats the leafs of the
CFTree as clusters.
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| BisectingKMeans |
The bisecting k-means algorithm works by starting with an initial
partitioning into two clusters, then repeated splitting of the largest
cluster to get additional clusters.
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| CompareMeans |
Compare-Means: Accelerated k-means by exploiting the triangle inequality and
pairwise distances of means to prune candidate means.
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| CompareMeans.Instance |
Inner instance, storing state for a single data set.
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| ElkanKMeans |
Elkan's fast k-means by exploiting the triangle inequality.
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| ExponionKMeans |
Newlings's Exponion k-means algorithm, exploiting the triangle inequality.
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| ExponionKMeans.Instance |
Inner instance, storing state for a single data set.
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| FuzzyCMeans |
Fuzzy Clustering developed by Dunn and revisited by Bezdek
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| GMeans |
G-Means extends K-Means and estimates the number of centers with Anderson
Darling Test.
Implemented as specialization of XMeans.
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| HamerlyKMeans |
Hamerly's fast k-means by exploiting the triangle inequality.
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| HamerlyKMeans.Instance |
Inner instance, storing state for a single data set.
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| HamerlyKMeans.Par |
Parameterization class.
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| HartiganWongKMeans |
Hartigan and Wong k-means clustering.
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| KDTreeFilteringKMeans |
Filtering or "blacklisting" K-means with k-d-tree acceleration.
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| KDTreePruningKMeans |
Pruning K-means with k-d-tree acceleration.
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| KDTreePruningKMeans.Instance |
Inner instance, storing state for a single data set.
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| KDTreePruningKMeans.KDNode |
Node of the k-d-tree used internally.
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| KDTreePruningKMeans.Par |
Parameterization class.
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| KDTreePruningKMeans.Split |
Splitting strategies for constructing the k-d-tree.
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| KMeans |
Some constants and options shared among kmeans family algorithms.
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| KMeansMinusMinus |
k-means--: A Unified Approach to Clustering and Outlier Detection.
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| KMediansLloyd |
k-medians clustering algorithm, but using Lloyd-style bulk iterations instead
of the more complicated approach suggested by Kaufman and Rousseeuw (see
PAM instead). |
| LloydKMeans |
The standard k-means algorithm, using bulk iterations and commonly attributed
to Lloyd and Forgy (independently).
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| MacQueenKMeans |
The original k-means algorithm, using MacQueen style incremental updates;
making this effectively an "online" (streaming) algorithm.
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| ShallotKMeans |
Borgelt's Shallot k-means algorithm, exploiting the triangle inequality.
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| SimplifiedElkanKMeans |
Simplified version of Elkan's k-means by exploiting the triangle inequality.
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| SimplifiedElkanKMeans.Instance |
Inner instance, storing state for a single data set.
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| SimplifiedElkanKMeans.Par |
Parameterization class.
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| SingleAssignmentKMeans |
Pseudo-k-means variations, that assigns each object to the nearest center.
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| SortMeans |
Sort-Means: Accelerated k-means by exploiting the triangle inequality and
pairwise distances of means to prune candidate means (with sorting).
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| XMeans |
X-means: Extending K-means with Efficient Estimation on the Number of
Clusters.
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| YinYangKMeans |
Yin-Yang k-Means Clustering.
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