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

Amorim, RC and Hennig, C (2015) 'Recovering the number of clusters in data sets with noise features using feature rescaling factors.' Information Sciences, 324. 126 - 145. ISSN 0020-0255

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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.

Item Type: Article
Uncontrolled Keywords: Feature re-scaling, Clustering, K-Means, Cluster validity index, Feature weighting
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 18 Sep 2017 13:46
Last Modified: 18 Oct 2017 16:16

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