Clustering noisy data in a reduced dimension space via multivariate regression trees
Smyth, Christine, Coomans, Danny, and Everingham, Yvette (2006) Clustering noisy data in a reduced dimension space via multivariate regression trees. Pattern Recognition, 39 (3). pp. 424-431.
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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.
|Item Type:||Article (Refereed Research - C1)|
|Keywords:||cluster analysis; statistics; regression trees; simulation; multivariate statistics|
|SEO Codes:||97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%|
|Deposited On:||28 Sep 2009 12:25|
|Last Modified:||14 Jun 2013 00:37|
Last 12 Months: 0
|Citation Counts with External Providers:||Web of Science: 8|
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