On Initializations for the Minkowski Weighted K-Means

Renato Cordeiro De Amorim, Peter Komisarczuk

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    16 Citations (Scopus)

    Abstract

    Minkowski Weighted K-Means is a variant of K-Means set in the Minkowski space, automatically computing weights for features at each cluster. As a variant of K-Means, its accuracy heavily depends on the initial centroids fed to it. In this paper we discuss our experiments comparing six initializations, random and five other initializations in the Minkowski space, in terms of their accuracy, processing time, and the recovery of the Minkowski exponent p.
    We have found that the Ward method in the Minkowski space tends to outperform other initializations, with the exception of low-dimensional Gaussian Models with noise features. In these, a modified version of intelligent K-Means excels.
    Original languageEnglish
    Title of host publicationAdvances in Intelligent Data Analysis XI
    PublisherSpringer Nature
    Pages45-55
    ISBN (Electronic)978-3-642-34156-4
    ISBN (Print)978-3-642-34155-7
    DOIs
    Publication statusPublished - 2012
    Event11th Int Symposium, IDA 2012 - Helsinki, Finland
    Duration: 25 Oct 201227 Oct 2012

    Publication series

    NameLecture Notes in Computer Science
    Volume7619

    Conference

    Conference11th Int Symposium, IDA 2012
    Country/TerritoryFinland
    CityHelsinki
    Period25/10/1227/10/12

    Fingerprint

    Dive into the research topics of 'On Initializations for the Minkowski Weighted K-Means'. Together they form a unique fingerprint.

    Cite this