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.Full text not available from this repository.
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) ,  and  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.
|Keyword(s) / Subject(s):||K-means, Minkowski metric, feature weights, noise features, anomalous cluster|
|School or Research Centre:||Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Information Systems|
|Date Deposited:||05 Sep 2011 09:43|
|Last Modified:||11 Oct 2016 11:58|
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