Uses of Package
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Packages that use de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Package	Description
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans	K-means clustering and variations.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization	Initialization strategies for k-means.
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel	Parallelized implementations of k-means.
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.

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans used by de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Class and Description
AbstractKMeans Abstract base class for k-means implementations.
AbstractKMeans.Parameterizer Parameterization class.
BestOfMultipleKMeans Run K-Means 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.
KMeans Some constants and options shared among kmeans family algorithms.
KMeansBatchedLloyd An algorithm for k-means, using Lloyd-style bulk iterations.
KMeansBisecting 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.
KMeansElkan Elkan's fast k-means by exploiting the triangle inequality.
KMeansHamerly Hamerly's fast k-means by exploiting the triangle inequality.
KMeansHybridLloydMacQueen A hybrid k-means algorithm, alternating between MacQueen-style incremental processing and Lloyd-Style batch steps.
KMeansLloyd The standard k-means algorithm, using Lloyd-style bulk iterations.
KMeansMacQueen The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.
KMediansLloyd k-medians clustering algorithm, but using Lloyd-style bulk iterations instead of the more complicated approach suggested by Kaufman and Rousseeuw (see KMedoidsPAM instead).
KMedoidsEM A k-medoids clustering algorithm, implemented as EM-style bulk algorithm.
KMedoidsPAM The original PAM algorithm or k-medoids clustering, as proposed by Kaufman and Rousseeuw in "Partitioning Around Medoids".
KMedoidsPAM.Parameterizer Parameterization class.
SingleAssignmentKMeans Pseudo-k-Means variations, that assigns each object to the nearest center.
XMeans X-means: Extending K-means with Efficient Estimation on the Number of Clusters.

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans used by de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization

Class and Description
KMeans Some constants and options shared among kmeans family algorithms.

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans used by de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel

Class and Description
AbstractKMeans Abstract base class for k-means implementations.
AbstractKMeans.Parameterizer Parameterization class.
KMeans Some constants and options shared among kmeans family algorithms.

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans used by de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain

Class and Description
KMeans Some constants and options shared among kmeans family algorithms.

Classes in de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans used by de.lmu.ifi.dbs.elki.algorithm.outlier.clustering

Class and Description
KMeans Some constants and options shared among kmeans family algorithms.