Clustering noisy data in a reduced dimension space via multivariate regression trees

Journal Publication ResearchOnline@JCU
Smyth, Christine;Coomans, Danny;Everingham, Yvette
Abstract

Cluster analysis is sensitive to noise variables intrinsically contained within high dimensional data sets. As the size of data sets increases, clustering techniques robust to noise variables must be identified. This investigation gauges the capabilities of recent clustering algorithms applied to two real data sets increasingly perturbed by superfluous noise variables. The recent techniques include mixture models of factor analysers and auto-associative multivariate regression trees. Statistical techniques are integrated to create two approaches useful for clustering noisy data: multivariate regression trees with principal component scores and multivariate regression trees with factor scores. The tree techniques generate the superior clustering results.

Journal

Pattern Recognition

Publication Name

N/A

Volume

39

ISBN/ISSN

1873-5142

Edition

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Issue

3

Pages Count

8

Location

N/A

Publisher

Pergamon

Publisher Url

N/A

Publisher Location

Oxford, United Kingdom

Publish Date

N/A

Url

N/A

Date

N/A

EISSN

N/A

DOI

10.1016/j.patcog.2005.09.003