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    Weighting features for partition around medoids using the minkowski metric

    Amorim, R.C. and Fenner, Trevor (2012) Weighting features for partition around medoids using the minkowski metric. In: Jaakko, H. and Frank, K. and Allan, T. (eds.) Advances in Intelligent Data Analysis. Lecture Notes in Computer Science 11 7619. Berlin, Germany: Springer Verlag, pp. 35-44. ISBN 9783642341557.

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    Abstract

    In this paper we introduce the Minkowski weighted partition around medoids algorithm (MW-PAM). This extends the popular partition around medoids algorithm (PAM) by automatically assigning K weights to each feature in a dataset, where K is the number of clusters. Our approach utilizes the within-cluster variance of features to calculate the weights and uses the Minkowski metric. We show through many experiments that MW-PAM, particularly when initialized with the Build algorithm (also using the Minkowski metric), is superior to other medoid-based algorithms in terms of both accuracy and identification of irrelevant features.

    Metadata

    Item Type: Book Section
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 16 May 2013 09:42
    Last Modified: 11 Oct 2016 15:26
    URI: http://eprints.bbk.ac.uk/id/eprint/6784

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