Package elki.clustering.em.models

Interface BetulaClusterModel

  • All Superinterfaces:
    EMClusterModel<elki.data.NumberVector,​EMModel>
    All Known Implementing Classes:
    DiagonalGaussianModel, MultivariateGaussianModel, SphericalGaussianModel
    @Reference(authors="Andreas Lang and Erich Schubert",
           title="BETULA: Fast Clustering of Large Data with Improved BIRCH CF-Trees",
           booktitle="Information Systems",
           url="https://doi.org/10.1016/j.is.2021.101918",
           bibkey="DBLP:journals/is/LangS22")
public interface BetulaClusterModel
extends EMClusterModel<elki.data.NumberVector,​EMModel>
    Models usable in Betula EM clustering.

    References:

    Andreas Lang and Erich Schubert
    BETULA: Fast Clustering of Large Data with Improved BIRCH CF-Trees
    Information Systems

    Since:
    0.8.0
    Author:
    Erich Schubert

    • Method Detail

      • estimateLogDensity

        double estimateLogDensity​(ClusterFeature cf)
        Estimate the log likelihood of a clustering feature.
        Parameters:
        cf - ClusteringFeature
        Returns:
        log likelihood

      • updateE

        void updateE​(ClusterFeature cf,
             double prob)
        Process one clustering feature in the E step.
        Parameters:
        cf - Clustering feature to process.
        prob - weight of the clustering feature.