Uses of Package
elki.clustering.kmedoids

Packages that use elki.clustering.kmedoids

Package	Description
elki.clustering.kmedoids	K-medoids clustering (PAM).
elki.clustering.kmedoids.initialization
elki.clustering.silhouette	Silhouette clustering algorithms.

Classes in elki.clustering.kmedoids used by elki.clustering.kmedoids

Class	Description
AlternatingKMedoids	A k-medoids clustering algorithm, implemented as EM-style batch algorithm; known in literature as the "alternate" method.
CLARA	Clustering Large Applications (CLARA) is a clustering method for large data sets based on PAM, partitioning around medoids (PAM) based on sampling.
CLARANS	CLARANS: a method for clustering objects for spatial data mining is inspired by PAM (partitioning around medoids, PAM) and CLARA and also based on sampling.
CLARANS.Assignment	Assignment state.
CLARANS.Par	Parameterization class.
EagerPAM	Variation of PAM that eagerly performs all swaps that yield an improvement during an iteration.
FastCLARA	Clustering Large Applications (CLARA) with the FastPAM 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.
FasterCLARA	Clustering Large Applications (CLARA) with the FastPAM improvements, to increase scalability in the number of clusters.
FasterPAM	Variation of FastPAM that eagerly performs any swap that yields an improvement during an iteration.
FasterPAM.Par	Parameterization class.
FastPAM	FastPAM: An improved version of PAM, that is usually O(k) times faster.
FastPAM.Instance	Instance for a single dataset.
FastPAM.Par	Parameterization class.
FastPAM1	FastPAM1: A version of PAM that is O(k) times faster, i.e., now in O((n-k)²).
FastPAM1.Instance	Instance for a single dataset.
FastPAM1.Par	Parameterization class.
KMedoidsClustering	Interface for clustering algorithms that produce medoids.
PAM	The original Partitioning Around Medoids (PAM) algorithm or k-medoids clustering, as proposed by Kaufman and Rousseeuw; a largely equivalent method was also proposed by Whitaker in the operations research domain, and is well known by the name "fast interchange" there.
PAM.Instance	Instance for a single dataset.
PAM.Par	Parameterization class.
ReynoldsPAM	The Partitioning Around Medoids (PAM) algorithm with some additional optimizations proposed by Reynolds et al.
SingleAssignmentKMedoids	K-medoids clustering by using the initialization only, then assigning each object to the nearest neighbor.

Classes in elki.clustering.kmedoids used by elki.clustering.kmedoids.initialization

Class	Description
KMedoidsClustering	Interface for clustering algorithms that produce medoids.

Classes in elki.clustering.kmedoids used by elki.clustering.silhouette

Class	Description
KMedoidsClustering	Interface for clustering algorithms that produce medoids.
PAM	The original Partitioning Around Medoids (PAM) algorithm or k-medoids clustering, as proposed by Kaufman and Rousseeuw; a largely equivalent method was also proposed by Whitaker in the operations research domain, and is well known by the name "fast interchange" there.
PAM.Par	Parameterization class.