Auto-associative multivariate regression trees for cluster analysis
Smyth, Christine, Coomans, Danny, Everingham, Yvette, and Hancock, Timothy (2006) Auto-associative multivariate regression trees for cluster analysis. Chemometrics and Intelligent Laboratory Systems, 80 (1). pp. 120-129.
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Multivariate Regression Trees, an intuitive and simple regression technique, intrinsically produce homogenous subsets of data. These characteristics imply that Multivariate Regression Trees have the potential to be utilised as an easily interpretable clustering method. The suitability of Multivariate Regression Trees as a clustering technique is investigated with two real datasets containing only explanatory variables. The preliminary results show that Multivariate Regression Trees as a clustering algorithm produce clusters of similar quality to the well-known K-means technique, and more recent approaches to Cluster Analysis including Mixture Models of Factor Analysers and Plaid Models. The study also evaluates the suitability of various criteria used to describe cluster solutions.
|Item Type:||Article (Refereed Research - C1)|
|Keywords:||cluster analysis; multivariate regression trees|
|SEO Codes:||97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 51%|
97 EXPANDING KNOWLEDGE > 970103 Expanding Knowledge in the Chemical Sciences @ 49%
|Deposited On:||15 Jun 2009 11:38|
|Last Modified:||25 May 2013 00:42|
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