Multiobjective Optimization of Co-Clustering Ensembles

• Abstract: Co-clustering is a machine learning task where the goal is to simultaneously develop clusters of the data and of their respective features. We address the use of co-clustering en- sembles to establish a consensus co-clustering over the data. In this paper we develop a new preference-based multiob- jective optimization algorithm to compete with a previous gradient ascent approach in nding optimal co-clustering ensembles. Unlike the gradient ascent algorithm, our ap- proach once tackles the co-clustering problem with multiple heuristics, then applies the gradient ascent algorithm's joint heuristic as a preference selection procedure. As a result, we are able to signicantly outperform the gradient ascent algorithm on feature clustering and on problems with smaller datasets.
• DOI: 10.1145/2330784.2331010
• ISBN: 978-1-4503-1178-6

F. Gullo, AKM Khaled Ahsan Talukder, S. Luke, C. Domeniconi, A. Tagarelli. Multiobjective Optimization of Co-Clustering Ensembles. Fourteenth International Conference on Genetic and Evolutionary Computation (GECCO), pp. 1495-1496. Philadelphia, USA, July 7-11, 2012.