A need has long been identified for a more effective methodology to understand, prevent, and cure cancer. Microarray technology provides a basis of achieving this goal, with cluster analysis of gene expression data leading to the discrimination of patients, identification of possible tumor subtypes, and individualized treatment. Recently, soft subspace clustering was introduced as an accurate alternative to conventional techniques. This practice has proven effective for high dimensional data, especially for microarray gene expressions. In this review, the basis of weighted dimensional space and different approaches to soft subspace clustering are described. Since most of the models are parameterized, the application of consensus clustering has been identified as a new research direction that is capable of turning the difficulty with parameter selection to an advantage of increasing diversity within an ensemble.
Multiple Suboptimal Solutions for Prediction Rules in Gene Expression Data
