University of Hertfordshire

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

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

  • Renato Cordeiro De Amorim
  • Boris Mirkin
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Original languageEnglish
Title of host publicationClusters, Orders, and Trees
Subtitle of host publicationMethods and Applications
ISBN (Electronic)978-1-4939-0742-7
ISBN (Print)978-1-4939-0741-0
Publication statusPublished - May 2014

Publication series

NameSpringer Optimization and Its Applications


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.

ID: 9822608