Class LloydKMeans<V extends NumberVector>

  • Type Parameters:
    V - vector datatype
    All Implemented Interfaces:
    Algorithm, ClusteringAlgorithm<Clustering<KMeansModel>>, KMeans<V,​KMeansModel>

    @Title("k-Means (Lloyd/Forgy Algorithm)")
    @Reference(authors="S. Lloyd",title="Least squares quantization in PCM",booktitle="IEEE Transactions on Information Theory 28 (2): 129\u2013137.",url="",bibkey="DBLP:journals/tit/Lloyd82") @Reference(authors="E. W. Forgy",title="Cluster analysis of multivariate data: efficiency versus interpretability of classifications",booktitle="Biometrics 21(3)",bibkey="journals/biometrics/Forgy65")
    public class LloydKMeans<V extends NumberVector>
    extends AbstractKMeans<V,​KMeansModel>
    The standard k-means algorithm, using bulk iterations and commonly attributed to Lloyd and Forgy (independently).


    S. Lloyd
    Least squares quantization in PCM
    IEEE Transactions on Information Theory 28 (2)
    previously published as Bell Telephone Laboratories Paper

    E. W. Forgy
    Cluster analysis of multivariate data: efficiency versus interpretability of classifications
    Abstract published in Biometrics 21(3)

    Arthur Zimek
    • Field Detail

      • LOG

        private static final Logging LOG
        The logger for this class.
    • Constructor Detail

      • LloydKMeans

        public LloydKMeans​(NumberVectorDistance<? super V> distance,
                           int k,
                           int maxiter,
                           KMeansInitialization initializer)
        distance - distance function
        k - k parameter
        maxiter - Maxiter parameter
        initializer - Initialization method