Mining Rules for the Automatic Selection Process of Clustering Methods Applied to Cancer Gene Expression Data

Different algorithms have been proposed in the literature to cluster gene expression data, however there is no single algorithm that can be considered the best one independently on the data. In this work, we applied the concepts of Meta-Learning to relate features of gene expression data sets to the performance of clustering algorithms. In our context, each meta-example represents descriptive features of a gene expression data set and a label indicating the best clustering algorithm when applied to the data. A set of such meta-examples is given as input to a learning technique (the meta-learner) which is responsible to acquire knowledge relating the descriptive features and the best algorithms. In our work, we performed experiments on a case study in which a metalearner was applied to discriminate among three competing algorithms for clustering gene expression data of cancer. In this case study, a set of meta-examples was generated from the application of the algorithms to 30 different...

• Type: Journal
• Year: 2009
• Where: ICANN
• Authors: André C. A. Nascimento, Ricardo Bastos Cavalcante Prudêncio, Marcílio Carlos Pereira de Souto, Ivan G. Costa
• Keywords: Algorithm, Descriptive Features, Gene Expression Data, ICANN 2009, Neural Networks