Package  Description 

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans 
Kmeans clustering and variations

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization 
Initialization strategies for kmeans.

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel 
Parallelized implementations of kmeans.

de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain 
Clustering algorithms for uncertain data.

de.lmu.ifi.dbs.elki.algorithm.outlier.clustering 
Clustering based outlier detection.

tutorial.clustering 
Classes from the tutorial on implementing a custom kmeans variation

Class and Description 

AbstractKMeans
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
Parameterization class.

BestOfMultipleKMeans
Run KMeans multiple times, and keep the best run.

CLARA
Clustering Large Applications (CLARA) is a clustering method for large data
sets based on PAM, partitioning around medoids (
KMedoidsPAM ) based on
sampling. 
CLARANS
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
Parameterization class.

FastCLARA
Clustering Large Applications (CLARA) with the
KMedoidsFastPAM
improvements, to increase scalability in the number of clusters. 
FastCLARANS
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.

KMeans
Some constants and options shared among kmeans family algorithms.

KMeansAnnulus
Annulus kmeans algorithm.

KMeansBisecting
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.

KMeansCompare
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.

KMeansElkan
Elkan's fast kmeans by exploiting the triangle inequality.

KMeansExponion
Newlings's exponion kmeans algorithm, exploiting the triangle inequality.

KMeansHamerly
Hamerly's fast kmeans by exploiting the triangle inequality.

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

KMeansHamerly.Parameterizer
Parameterization class.

KMeansLloyd
The standard kmeans algorithm, using bulk iterations and commonly attributed
to Lloyd and Forgy (independently).

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

KMeansMinusMinus
kmeans: A Unified Approach to Clustering and Outlier Detection.

KMeansSimplifiedElkan
Simplified version of Elkan's kmeans by exploiting the triangle inequality.

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

KMeansSimplifiedElkan.Parameterizer
Parameterization class.

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

KMediansLloyd
kmedians clustering algorithm, but using Lloydstyle bulk iterations instead
of the more complicated approach suggested by Kaufman and Rousseeuw (see
KMedoidsPAM instead). 
KMedoidsFastPAM
FastPAM: An improved version of PAM, that is usually O(k) times faster.

KMedoidsFastPAM.Parameterizer
Parameterization class.

KMedoidsFastPAM1
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
Parameterization class.

KMedoidsPAM
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
Parameterization class.

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

KMedoidsPark
A kmedoids clustering algorithm, implemented as EMstyle bulk algorithm.

SingleAssignmentKMeans
PseudokMeans variations, that assigns each object to the nearest center.

XMeans
Xmeans: Extending Kmeans with Efficient Estimation on the Number of
Clusters.

Class and Description 

KMeans
Some constants and options shared among kmeans family algorithms.

Class and Description 

AbstractKMeans
Abstract base class for kmeans implementations.

AbstractKMeans.Parameterizer
Parameterization class.

KMeans
Some constants and options shared among kmeans family algorithms.

Class and Description 

KMeans
Some constants and options shared among kmeans family algorithms.

Class and Description 

KMeans
Some constants and options shared among kmeans family algorithms.

Class and Description 

AbstractKMeans
Abstract base class for kmeans implementations.

KMeans
Some constants and options shared among kmeans family algorithms.

Copyright © 2019 ELKI Development Team. License information.