Clustering noisy data in a reduced dimension space via multivariate regression trees
Journal Publication ResearchOnline@JCUAbstract
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
N/A
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