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

Renato Cordeiro De Amorim, Boris Mirkin

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

    5 Citations (Scopus)

    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.
    Original languageEnglish
    Title of host publicationClusters, Orders, and Trees
    Subtitle of host publicationMethods and Applications
    PublisherSpringer Nature
    Pages103-117
    ISBN (Electronic)978-1-4939-0742-7
    ISBN (Print)978-1-4939-0741-0
    DOIs
    Publication statusPublished - May 2014

    Publication series

    NameSpringer Optimization and Its Applications
    Volume92

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