Class FDBSCAN

  • All Implemented Interfaces:
    Algorithm, ClusteringAlgorithm<Clustering<Model>>

    @Title("FDBSCAN: Density-based Clustering of Applications with Noise on fuzzy objects")
    @Description("Algorithm to find density-connected sets in a database consisting of uncertain/fuzzy objects based on the parameters \'minpts\', \'epsilon\', \'samplesize\', and (if used) \'threshold\'")
    @Reference(authors="Hans-Peter Kriegel, Martin Pfeifle",
               title="Density-based clustering of uncertain data",
               booktitle="Proc. 11th ACM Int. Conf. on Knowledge Discovery and Data Mining (SIGKDD)",
               url="https://doi.org/10.1145/1081870.1081955",
               bibkey="DBLP:conf/kdd/KriegelP05")
    public class FDBSCAN
    extends GeneralizedDBSCAN
    FDBSCAN is an adaption of DBSCAN for fuzzy (uncertain) objects.

    This implementation is based on GeneralizedDBSCAN. All implementation of FDBSCAN functionality is located in the neighbor predicate FDBSCANNeighborPredicate.

    Reference:

    Hans-Peter Kriegel, Martin Pfeifle
    Density-based clustering of uncertain data
    Proc. 11th ACM Int. Conf. on Knowledge Discovery and Data Mining (SIGKDD)

    Since:
    0.7.0
    Author:
    Erich Schubert
    • Constructor Detail

      • FDBSCAN

        public FDBSCAN​(double epsilon,
                       int sampleSize,
                       double threshold,
                       RandomFactory seed,
                       int minpts)
        Constructor that initialized GeneralizedDBSCAN.
        Parameters:
        epsilon - Epsilon radius
        sampleSize - Sample size
        threshold - Threshold
        seed - Random generator
        minpts - MinPts