A Multi-objective Multipopulation Approach For Biclustering

Biclustering is a technique developed to allow simultaneous clustering of rows and columns of a dataset. This might be useful to extract more accurate information from sparse datasets and to avoid some of the drawbacks presented by standard clustering techniques, such as their impossibility of finding correlating data under a subset of features. Given that biclustering requires the optimization of two conflicting objectives (residue and volume) and that multiple independent solutions are desirable as the outcome, a multi-objective artificial immune system capable of performing a multipopulation search, named MOM-aiNet, will be proposed in this paper. To illustrate the capabilities of this novel algorithm, MOM-aiNet was applied to the extraction of biclusters from two datasets, one taken from a well-known gene expression problem and the other from a collaborative filtering application. A comparative analysis has also been accomplished, with the obtained results being confronted with the ones produced by two popular biclustering algorithms from the literature (FLOC and CC) and also by another immune-inspired approach for biclustering (BIC-aiNet). © 2008 Springer-Verlag Berlin Heidelberg.5132 LNCS7182de França, F.O., Bezerra, G., Von Zuben, F.J., New Perspectives for the Biclustering Problem (2006) Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 753-760. , Vancouver, Canada, ppCheng, Y., Church, G.M., Biclustering of expression data (2000) Proc. of the 8th Int. Conf. on Intelligent Systems for Molecular Biology, pp. 93-103de Castro, L.N., Von Zuben, F.J.: aiNet: An Artificial Immune Network for Data Analysis. In: Abbass, H.A., Sarker, R.A., Newton, C.S. (eds.) Data Mining: A Heuristic Approach, pp. 231-259. Idea Group Publishing (2001)Cho, R., Campbell, M., Winzeler, E., Steinmetz, L., Conway, A., Wodicka, L., Wolfsberg, T., Davis, R., A genome-wide transcriptional analysis of the mitotic cell cycle (1998) Molecular Cell, 2, pp. 65-73GroupLens, MovieLens dataset, , http://www.grouplens.org/node/73, Department of Computer Science and Engineering, University of MinnesotaJiong, Y., Haixun, W., Wei, W., Yu, P.S., Enhanced biclustering on expression data (2003) Proc. of the Third IEEE Symposium on Bioinformatics and Bioengineering, pp. 321-327de Castro, P.A.D., de França, F.O., Ferreira, H.M., Von Zuben, F.J.: Applying Biclustering to Text Mining: An Immune-Inspired Approach. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) ICARIS 2007. LNCS, 4628, pp. 83-94. Springer, Heidelberg (2007)de Castro, P.A.D., de França, F.O., Ferreira, H.M., Von Zuben, F.J., Applying Biclustering to Perform Collaborative Filtering (2007) Proc. of the 7th International Conference on Intelligent Systems Design and Applications, pp. 421-426. , Rio de Janeiro, Brazil, ppde Castro, P.A.D., de França, F.O., Ferreira, H.M., Von Zuben, F.J., Evaluating the Performance of a Biclustering Algorithm Applied to Collaborative Filtering: A Comparative Analysis (2007) Proc. of the 7th International Conference on Hybrid Intelligent Systems, pp. 65-70. , Kaiserslautern, Germany, ppAgrawal, R., Gehrke, J., Gunopulus, D., Raghavan, P., Automatic subspace clustering of high dimensional data for data mining applications (1998) Proc. of the ACM/SIGMOD Int. Conference on Management of Data, pp. 94-105Dhillon, I.S., Co-clustering documents and words using bipartite spectral graph partitioning (2001) Proc. of the 7th Int. Con. on Knowledge Discovery and Data Mining, pp. 269-274Feldman, R., Sanger, J., (2006) The Text Mining Handbook, , Cambridge University Press, CambridgeHartigan, J.A., Direct clustering of a data matrix (1972) Journal of the American Statistical Association (JASA), 67 (337), pp. 123-129Symeonidis, P., Nanopoulos, A., Papadopoulos, A., Manolopoulos, Y., Nearestbiclusters collaborative filtering with constant values (2007) LNCS, 4811, pp. 36-55. , Advances in Web Mining and Web Usage Analysis, Philadelphia, USA, Springer, HeidelbergTang, C., Zhang, L., Zhang, I., Ramanathan, M., Interrelated two-way clustering: An unsupervised approach for gene expression data analysis (2001) Proc. of the 2nd IEEE Int. Symposium on Bioinformatics and Bioengineering, pp. 41-48Madeira, S.C., Oliveira, A.L., Biclustering algorithms for biological data analysis: A survey (2004) IEEE Transactions on Computational Biology and Bioinformatics, 1 (1), pp. 24-45Burnet, F.M., Clonal selection and after (1978) Theoretical Immunology, pp. 63-85. , Bell, G.I, Perelson, A.S, Pimgley Jr, G.H, eds, Marcel Dekker Inc, New YorkJerne, N.K.: Towards a network theory of the immune system. Ann. Immunol., Inst. Pasteur 125C, 373-389 (1974)de Castro, L.N., Von Zuben, F.J., Learning and Optimization Using the Clonal Selection Principle (2002) IEEE Transactions on Evolutionary Computation, 6 (3), pp. 239-251Deb, K., (2001) Multi-Objective Optimization Using Evolutionary Algorithms, , Wiley, ChichesterCoelho, G.P., Von Zuben, F. J.: omni-aiNet: An immune-inspired approach for omni optimization. In: Bersini, H., Carneiro, J. (eds.) ICARIS 2006. LNCS, 4163, pp. 294-308. Springer, Heidelberg (2006)Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II (2002) IEEE Transactions on Evolutionary Computation, 6 (2), pp. 182-19