Selecting the Minkowski Exponent for Intelligent K-Means with Feature Weighting

Renato Cordeiro Amorim () and Boris Mirkin ()
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Renato Cordeiro Amorim: Glyndŵr University
Boris Mirkin: National Research University Higher School of Economics

A chapter in Clusters, Orders, and Trees: Methods and Applications, 2014, pp 103-117 from Springer

Abstract: Abstract Recently, a three-stage version of K-Means has been introduced, at which not only clusters and their centers, but also feature weights are adjusted to minimize the summary p-th power of the Minkowski p-distance between entities and centroids of their clusters. The value of the Minkowski exponent p appears to be instrumental in the ability of the method to recover clusters hidden in data. This paper advances into the problem of finding the best p for a Minkowski metric-based version of K-Means, in each of the following two settings: semi-supervised and unsupervised. This paper presents experimental evidence that solutions found with the proposed approaches are sufficiently close to the optimum.

Keywords: Clustering; Minkowski metric; Feature weighting; K-Means (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-1-4939-0742-7_7

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DOI: 10.1007/978-1-4939-0742-7_7

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