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    The Minkowski central partition as a pointer to a suitable distance exponent and consensus partitioning

    Cordeiro de Amorim, Renato and Shestakov, A. and Mirkin, Boris and Makarenkov, V. (2017) The Minkowski central partition as a pointer to a suitable distance exponent and consensus partitioning. Pattern Recognition 67 , pp. 62-72. ISSN 0031-3203.

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    Abstract

    The Minkowski weighted K-means (MWK-means) is a recently developed clustering algorithm capable of computing feature weights. The cluster-specific weights in MWK-means follow the intuitive idea that a feature with low variance should have a greater weight than a feature with high variance. The final clustering found by this algorithm depends on the selection of the Minkowski distance exponent. This paper explores the possibility of using the central Minkowski partition in the ensemble of all Minkowski partitions for selecting an optimal value of the Minkowski exponent. The central Minkowski partition appears to be also a good consensus partition. Furthermore, we discovered some striking correlation results between the Minkowski profile, defined as a mapping of the Minkowski exponent values into the average similarity values of the optimal Minkowski partitions, and the Adjusted Rand Index vectors resulting from the comparison of the obtained partitions to the ground truth. Our findings were confirmed by a series of computational experiments involving synthetic Gaussian clusters and real-world data.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Clustering, Central clustering, Feature weighting, Minkowski metric, Minkowski ensemble
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Administrator
    Date Deposited: 29 Jun 2017 09:25
    Last Modified: 03 Sep 2018 12:44
    URI: http://eprints.bbk.ac.uk/id/eprint/18999

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