Multiobjective optimization of co-clustering ensembles

Authors:
Francesco Gullo;AKM Khaled Talukder;Sean Luke;Carlotta Domeniconi;Andrea Tagarelli
Affiliations:
Yahoo!, Barcelona, Spain;George Mason University, Washington, DC, USA;George Mason University, Washington, DC, USA;George Mason University, Washington, DC, USA;Università di Calabria, Cosenza, Italy
Venue:
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Year:
2012

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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 ensembles to establish a consensus co-clustering over the data. In this paper we develop a new preference-based multiobjective optimization algorithm to compete with a previous gradient ascent approach in finding optimal co-clustering ensembles. Unlike the gradient ascent algorithm, our approach once tackles the co-clustering problem with multiple heuristics, then applies the gradient ascent algorithm's joint heuristic as a preference selection procedure. We are able to significantly outperform the gradient ascent algorithm on feature clustering and on problems with smaller datasets.