See: Description
Interface  Description 

KMeans<V extends NumberVector,M extends Model> 
Some constants and options shared among kmeans family algorithms.

Class  Description 

AbstractKMeans<V extends NumberVector,M extends Model> 
Abstract base class for kmeans implementations.

AbstractKMeans.Instance 
Inner instance for a run, for better encapsulation, that encapsulates the
standard flow of most (but not all) kmeans variations.

AbstractKMeans.Parameterizer<V extends NumberVector> 
Parameterization class.

BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel> 
Run KMeans multiple times, and keep the best run.

BestOfMultipleKMeans.Parameterizer<V extends NumberVector,M extends MeanModel> 
Parameterization class.

CLARA<V> 
Clustering Large Applications (CLARA) is a clustering method for large data
sets based on PAM, partitioning around medoids (
KMedoidsPAM ) based on
sampling. 
CLARA.CachedDistanceQuery<V> 
Cached distance query.

CLARA.Parameterizer<V> 
Parameterization class.

CLARANS<V> 
CLARANS: a method for clustering objects for spatial data mining
is inspired by PAM (partitioning around medoids,
KMedoidsPAM )
and CLARA and also based on sampling. 
CLARANS.Assignment 
Assignment state.

CLARANS.Parameterizer<V> 
Parameterization class.

FastCLARA<V> 
Clustering Large Applications (CLARA) with the
KMedoidsFastPAM
improvements, to increase scalability in the number of clusters. 
FastCLARA.Parameterizer<V> 
Parameterization class.

FastCLARANS<V> 
A faster variation of CLARANS, that can explore O(k) as many swaps at a
similar cost by considering all medoids for each candidate nonmedoid.

FastCLARANS.Assignment 
Assignment state.

FastCLARANS.Parameterizer<V> 
Parameterization class.

KMeansAnnulus<V extends NumberVector> 
Annulus kmeans algorithm.

KMeansAnnulus.Instance 
Inner instance, storing state for a single data set.

KMeansAnnulus.Parameterizer<V extends NumberVector> 
Parameterization class.

KMeansBisecting<V extends NumberVector,M extends MeanModel> 
The bisecting kmeans algorithm works by starting with an initial
partitioning into two clusters, then repeated splitting of the largest
cluster to get additional clusters.

KMeansBisecting.Parameterizer<V extends NumberVector,M extends MeanModel> 
Parameterization class.

KMeansCompare<V extends NumberVector> 
CompareMeans: Accelerated kmeans by exploiting the triangle inequality and
pairwise distances of means to prune candidate means.

KMeansCompare.Instance 
Inner instance, storing state for a single data set.

KMeansCompare.Parameterizer<V extends NumberVector> 
Parameterization class.

KMeansElkan<V extends NumberVector> 
Elkan's fast kmeans by exploiting the triangle inequality.

KMeansElkan.Instance 
Inner instance, storing state for a single data set.

KMeansElkan.Parameterizer<V extends NumberVector> 
Parameterization class.

KMeansExponion<V extends NumberVector> 
Newlings's exponion kmeans algorithm, exploiting the triangle inequality.

KMeansExponion.Instance 
Inner instance, storing state for a single data set.

KMeansExponion.Parameterizer<V extends NumberVector> 
Parameterization class.

KMeansHamerly<V extends NumberVector> 
Hamerly's fast kmeans by exploiting the triangle inequality.

KMeansHamerly.Instance 
Inner instance, storing state for a single data set.

KMeansHamerly.Parameterizer<V extends NumberVector> 
Parameterization class.

KMeansLloyd<V extends NumberVector> 
The standard kmeans algorithm, using bulk iterations and commonly attributed
to Lloyd and Forgy (independently).

KMeansLloyd.Instance 
Inner instance, storing state for a single data set.

KMeansLloyd.Parameterizer<V extends NumberVector> 
Parameterization class.

KMeansMacQueen<V extends NumberVector> 
The original kmeans algorithm, using MacQueen style incremental updates;
making this effectively an "online" (streaming) algorithm.

KMeansMacQueen.Instance 
Inner instance, storing state for a single data set.

KMeansMacQueen.Parameterizer<V extends NumberVector> 
Parameterization class.

KMeansMinusMinus<V extends NumberVector> 
kmeans: A Unified Approach to Clustering and Outlier Detection.

KMeansMinusMinus.Parameterizer<V extends NumberVector> 
Parameterization class.

KMeansSimplifiedElkan<V extends NumberVector> 
Simplified version of Elkan's kmeans by exploiting the triangle inequality.

KMeansSimplifiedElkan.Instance 
Inner instance, storing state for a single data set.

KMeansSimplifiedElkan.Parameterizer<V extends NumberVector> 
Parameterization class.

KMeansSort<V extends NumberVector> 
SortMeans: Accelerated kmeans by exploiting the triangle inequality and
pairwise distances of means to prune candidate means (with sorting).

KMeansSort.Instance 
Inner instance, storing state for a single data set.

KMeansSort.Parameterizer<V extends NumberVector> 
Parameterization class.

KMediansLloyd<V extends NumberVector> 
kmedians clustering algorithm, but using Lloydstyle bulk iterations instead
of the more complicated approach suggested by Kaufman and Rousseeuw (see
KMedoidsPAM instead). 
KMediansLloyd.Instance 
Inner instance, storing state for a single data set.

KMediansLloyd.Parameterizer<V extends NumberVector> 
Parameterization class.

KMedoidsFastPAM<V> 
FastPAM: An improved version of PAM, that is usually O(k) times faster.

KMedoidsFastPAM.Instance 
Instance for a single dataset.

KMedoidsFastPAM.Parameterizer<V> 
Parameterization class.

KMedoidsFastPAM1<V> 
FastPAM1: A version of PAM that is O(k) times faster, i.e., now in O((nk)²).

KMedoidsFastPAM1.Instance 
Instance for a single dataset.

KMedoidsFastPAM1.Parameterizer<V> 
Parameterization class.

KMedoidsPAM<V> 
The original Partitioning Around Medoids (PAM) algorithm or kmedoids
clustering, as proposed by Kaufman and Rousseeuw in "Clustering by means of
Medoids".

KMedoidsPAM.Instance 
Instance for a single dataset.

KMedoidsPAM.Parameterizer<V> 
Parameterization class.

KMedoidsPAMReynolds<V> 
The Partitioning Around Medoids (PAM) algorithm with some additional
optimizations proposed by Reynolds et al.

KMedoidsPAMReynolds.Instance 
Instance for a single dataset.

KMedoidsPAMReynolds.Parameterizer<V> 
Parameterization class.

KMedoidsPark<V> 
A kmedoids clustering algorithm, implemented as EMstyle bulk algorithm.

KMedoidsPark.Parameterizer<V> 
Parameterization class.

SingleAssignmentKMeans<V extends NumberVector> 
PseudokMeans variations, that assigns each object to the nearest center.

SingleAssignmentKMeans.Instance 
Inner instance, storing state for a single data set.

SingleAssignmentKMeans.Parameterizer<V extends NumberVector> 
Parameterization class.

XMeans<V extends NumberVector,M extends MeanModel> 
Xmeans: Extending Kmeans with Efficient Estimation on the Number of
Clusters.

XMeans.Parameterizer<V extends NumberVector,M extends MeanModel> 
Parameterization class.

Copyright © 2019 ELKI Development Team. License information.