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.
tutorial.clustering	Classes from the tutorial on implementing a custom k-means variation

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.Instance Inner instance for a run, for better encapsulation, that encapsulates the standard flow of most (but not all) k-means variations.
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.
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 non-medoid.
KMeans Some constants and options shared among kmeans family algorithms.
KMeansAnnulus Annulus k-means algorithm.
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.
KMeansCompare Compare-Means: Accelerated k-means 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 k-means by exploiting the triangle inequality.
KMeansExponion Newlings's exponion k-means algorithm, exploiting the triangle inequality.
KMeansHamerly Hamerly's fast k-means by exploiting the triangle inequality.
KMeansHamerly.Instance Inner instance, storing state for a single data set.
KMeansHamerly.Parameterizer Parameterization class.
KMeansLloyd The standard k-means algorithm, using bulk iterations and commonly attributed to Lloyd and Forgy (independently).
KMeansMacQueen The original k-means algorithm, using MacQueen style incremental updates; making this effectively an "online" (streaming) algorithm.
KMeansMinusMinus k-means--: A Unified Approach to Clustering and Outlier Detection.
KMeansSimplifiedElkan Simplified version of Elkan's k-means by exploiting the triangle inequality.
KMeansSimplifiedElkan.Instance Inner instance, storing state for a single data set.
KMeansSimplifiedElkan.Parameterizer Parameterization class.
KMeansSort Sort-Means: Accelerated k-means by exploiting the triangle inequality and pairwise distances of means to prune candidate means (with sorting).
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).
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((n-k)²).
KMedoidsFastPAM1.Instance Instance for a single dataset.
KMedoidsFastPAM1.Parameterizer Parameterization class.
KMedoidsPAM The original Partitioning Around Medoids (PAM) algorithm or k-medoids 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 k-medoids clustering algorithm, implemented as EM-style bulk algorithm.
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.

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

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