de Amorim, RC;
Hennig, C;
(2015)
Recovering the number of clusters in data sets with noise features using feature rescaling factors.
Information Sciences
, 324
pp. 126-145.
10.1016/j.ins.2015.06.039.
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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.
| Type: | Article |
|---|---|
| Title: | Recovering the number of clusters in data sets with noise features using feature rescaling factors |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ins.2015.06.039 |
| Publisher version: | http://dx.doi.org/10.1016/j.ins.2015.06.039 |
| Language: | English |
| Additional information: | © 2015 Elsevier Inc. All rights reserved. This manuscript is made available under a Creative Commons Attribution Non-commercial Non-derivative 4.0 International license (CC BY-NC-ND 4.0). This license allows you to share, copy, distribute and transmit the work for personal and non-commercial use providing author and publisher attribution is clearly stated. Further details about CC BY licenses are available at http://creativecommons.org/ licenses/by/4.0. Access may be initially restricted by the publisher. |
| Keywords: | Feature re-scaling, Clustering, K-Means, Cluster validity index, Feature weighting |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1475072 |
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