University of Hertfordshire

From the same journal

Recovering the number of clusters in data sets with noise features using feature rescaling factors

Research output: Contribution to journalArticlepeer-review

Documents

  • Renato Cordeiro De Amorim
  • Christian Hennig
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Original languageEnglish
Pages (from-to)126-145
JournalInformation Sciences
Volume324
Early online date30 Jun 2015
DOIs
Publication statusPublished - 10 Dec 2015

Abstract

In this paper we introduce three methods for re-scaling data sets aiming at improving the likelihood of clustering validity indexes to return the true number of spherical Gaussian clusters with additional noise features. Our method obtains feature re-scaling factors taking into account the structure of a given data set and the intuitive idea that different features may have different degrees of relevance at different clusters.
We experiment with the Silhouette (using squared Euclidean, Manhattan, and the pth power of the Minkowski distance), Dunn’s, Calinski–Harabasz and Hartigan indexes on data sets with spherical Gaussian clusters with and without noise features. We conclude that our methods indeed increase the chances of estimating the true number of clusters in a data set.

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