Class RandomlyChosen<O>

  • Type Parameters:
    O - Vector type
    All Implemented Interfaces:
    KMeansInitialization, KMedoidsInitialization<O>

    @Reference(authors="D. J. McRae",title="MIKCA: A FORTRAN IV Iterative K-Means Cluster Analysis Program",booktitle="Behavioral Science 16(4)",bibkey="journals/misc/McRae71") @Reference(authors="E. W. Forgy",title="Cluster analysis of multivariate data: efficiency versus interpretability of classifications",booktitle="Biometrics 21(3)",bibkey="journals/biometrics/Forgy65") @Reference(authors="M. R. Anderberg",title="Nonhierarchical Clustering Methods",booktitle="Cluster Analysis for Applications",bibkey="books/academic/Anderberg73/Ch7")
    public class RandomlyChosen<O>
    extends AbstractKMeansInitialization
    implements KMedoidsInitialization<O>
    Initialize K-means by randomly choosing k existing elements as initial cluster centers.

    Reference:

    D. J. McRae
    MIKCA: A FORTRAN IV Iterative K-Means Cluster Analysis Program
    Behavioral Science 16(4)

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

    M. R. Anderberg
    Hierarchical Clustering Methods
    Cluster Analysis for Applications

    This initialization is often attributed to Forgy (but this is also debated), but we were unable to verify neither McRae (not available online?) nor Forgy so far (apparently, only an abstract is available in print, so we mostly can rely on indirect references, such as Anderberg).

    Since:
    0.5.0
    Author:
    Erich Schubert