Ranking and selecting clustering algorithms using a meta-learning approach

Abstract— 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.

• Type: Conference
• Year: 2008
• Where: IJCNN
• Authors: Marcílio Carlos Pereira de Souto, Ricardo Bastos Cavalcante Prudêncio, Rodrigo G. F. Soares, Daniel S. A. de Araujo, Ivan G. Costa, Teresa Bernarda Ludermir, Alexander Schliep
• Keywords: Algorithm Selection Task, Artificial Intelligence, Expression Microarray Datasets, IJCNN 2008, Regression Support Vector