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    Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering

    Cordeiro de Amorim, Renato and Mirkin, Boris (2012) Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering. Pattern Recognition 45 (3), pp. 1061-1075. ISSN 0031-3203.

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    Abstract

    This paper represents another step in overcoming a drawback of K-Means, its lack of defense against noisy features, using feature weights in the criterion. The Weighted K-Means method by Huang et al. (2008, 2004, 2005) [5], [6] and [7] is extended to the corresponding Minkowski metric for measuring distances. Under Minkowski metric the feature weights become intuitively appealing feature rescaling factors in a conventional K-Means criterion. To see how this can be used in addressing another issue of K-Means, the initial setting, a method to initialize K-Means with anomalous clusters is adapted. The Minkowski metric based method is experimentally validated on datasets from the UCI Machine Learning Repository and generated sets of Gaussian clusters, both as they are and with additional uniform random noise features, and appears to be competitive in comparison with other K-Means based feature weighting algorithms.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): K-means, Minkowski metric, feature weights, noise features, anomalous cluster
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Administrator
    Date Deposited: 05 Sep 2011 09:43
    Last Modified: 11 Oct 2016 11:58
    URI: http://eprints.bbk.ac.uk/id/eprint/4102

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