Ranking and Selecting Clustering Algorithms Using a Meta-learning Approach

M.C.P. de Souto, R.B.C. Prudencio, R.G.F. Soares, D.A.S. Araujo, I.G. Costa, T.B. Ludermir and A. Schliep

In Proceedings of the International Joint Conference on Neural Networks, IEEE Computer Society, 2008.

We present a novel framework that applies a metalearning approach to clustering algorithms. Given a dataset, our meta-learning approach provides a ranking for the candidate algorithms that could be used with that dataset. This ranking could, among other things, support non-expert users in the algorithm selection task. In order to evaluate the framework proposed, we implement a prototype that employs regression support vector machines as the meta-learner. Our case study is developed in the context of cancer gene expression microarray datasets.

A reprint is available as PDF.

DOI: 10.1109/IJCNN.2008.4634333.

The publication includes results from the following projects or software tools: MASCAAT.

Further publications by Alexander Schliep, Ivan G Costa, Marcilio C. Pereira de Souto.