Package elki.clustering.kmedoids
K-medoids clustering (PAM).
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Interface Summary Interface Description KMedoidsClustering<O> Interface for clustering algorithms that produce medoids. -
Class Summary Class Description AlternatingKMedoids<O> A k-medoids clustering algorithm, implemented as EM-style batch algorithm; known in literature as the "alternate" method.AlternatingKMedoids.Par<V> Parameterization class.CLARA<V> Clustering Large Applications (CLARA) is a clustering method for large data sets based on PAM, partitioning around medoids (PAM
) based on sampling.CLARA.CachedDistanceQuery<V> Cached distance query.CLARA.Par<V> Parameterization class.CLARANS<O> 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<V> Parameterization class.EagerPAM<O> Variation of PAM that eagerly performs all swaps that yield an improvement during an iteration.EagerPAM.Instance Instance for a single dataset.EagerPAM.Par<O> Parameterization class.FastCLARA<V> Clustering Large Applications (CLARA) with theFastPAM
improvements, to increase scalability in the number of clusters.FastCLARA.Par<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 non-medoid.FastCLARANS.Assignment Assignment state.FastCLARANS.Par<V> Parameterization class.FasterCLARA<O> Clustering Large Applications (CLARA) with theFastPAM
improvements, to increase scalability in the number of clusters.FasterCLARA.Par<V> Parameterization class.FasterPAM<O> Variation of FastPAM that eagerly performs any swap that yields an improvement during an iteration.FasterPAM.Instance Instance for a single dataset.FasterPAM.Par<O> Parameterization class.FastPAM<O> FastPAM: An improved version of PAM, that is usually O(k) times faster.FastPAM.Instance Instance for a single dataset.FastPAM.Par<V> Parameterization class.FastPAM1<O> 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<V> Parameterization class.PAM<O> 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<O> Parameterization class.ReynoldsPAM<O> The Partitioning Around Medoids (PAM) algorithm with some additional optimizations proposed by Reynolds et al.ReynoldsPAM.Instance Instance for a single dataset.ReynoldsPAM.Par<V> Parameterization class.SingleAssignmentKMedoids<O> K-medoids clustering by using the initialization only, then assigning each object to the nearest neighbor.SingleAssignmentKMedoids.Instance Instance for a single dataset.SingleAssignmentKMedoids.Par<O> Parameterization class.