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dc.contributor.authorCordeiro De Amorim, Renato
dc.contributor.authorMirkin, Boris
dc.identifier.citationCordeiro De Amorim , R & Mirkin , B 2014 , Selecting the Minkowski Exponent for Intelligent K-Means with Feature Weighting . in Clusters, Orders, and Trees : Methods and Applications . Springer Optimization and Its Applications , vol. 92 , Springer , pp. 103-117 .
dc.identifier.otherPURE: 9822608
dc.identifier.otherPURE UUID: 20195da1-512a-4358-b2b8-efe1cc8a4990
dc.identifier.otherScopus: 85029650998
dc.description.abstractRecently, 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.en
dc.relation.ispartofClusters, Orders, and Trees
dc.relation.ispartofseriesSpringer Optimization and Its Applications
dc.titleSelecting the Minkowski Exponent for Intelligent K-Means with Feature Weightingen
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed

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