Class KMeansMinusMinusOutlierDetection

  • All Implemented Interfaces:
    Algorithm, OutlierAlgorithm

    @Title("K-Means--")
    @Reference(authors="S. Chawla, A. Gionis",
               title="k-means--: A Unified Approach to Clustering and Outlier Detection",
               booktitle="Proc. 13th SIAM Int. Conf. on Data Mining (SDM 2013)",
               url="https://doi.org/10.1137/1.9781611972832.21",
               bibkey="DBLP:conf/sdm/ChawlaG13")
    public class KMeansMinusMinusOutlierDetection
    extends NoiseAsOutliers
    k-means--: A Unified Approach to Clustering and Outlier Detection.

    This implementation assigns the outlier label to all points that were identified as noise according to the KMeans-- algorithm.

    Reference:

    S. Chawla, A. Gionis
    k-means--: A Unified Approach to Clustering and Outlier Detection
    Proc. 13th SIAM Int. Conf. on Data Mining (SDM 2013)

    Since:
    0.8.0
    Author:
    Braulio V.S. Vinces (ELKIfication)