Abstract
This paper deals with overlapping clustering and presents two extensions of the approach OKM denoted as OKMED andWOKM. OKMED generalizes the well known k-medoid method to overlapping clustering and help in organizing data with any proximity matrix as input. WOKM (Weighted-OKM) proposes a model with local weighting of the clusters; this variant is suitable for overlapping clustering since a single data can matches with multiple classes according to different features. On text clustering, we show that OKMED has a behavior similar to OKM but offers to use metrics other than euclidean distance. Then we observe significant improvement using the weighted extension of OKM.
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Cleuziou, G. (2010). Two Variants of the OKM for Overlapping Clustering. In: Guillet, F., Ritschard, G., Zighed, D.A., Briand, H. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00580-0_9
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DOI: https://doi.org/10.1007/978-3-642-00580-0_9
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