Clustering objects on subsets of attributes
A new procedure is proposed for clustering attribute value data. When used in conjunction with conventional distance-based clustering algorithms this procedure encourages those algorithms to detect automatically subgroups of objects that preferentially cluster on subsets of the attribute variables rather than on all of them simultaneously. The relevant attribute subsets for each individual cluster can be different and partially (or completely) overlap with those of other clusters. Enhancements for increasing sensitivity for detecting especially low cardinality groups clustering on a small subset of variables are discussed. Applications in different domains, including gene expression arrays, are presented.
Keywords: Bioinformatics; Clustering on variable subsets; Distance-based clustering; Feature selection; Gene expression microarray data; Genomics; Inverse exponential distance; Mixtures of numeric and categorical variables; Targeted clustering
Document Type: Research Article
Publication date: 2004-11-01