By K. S. Machado, E. K. Schroeder, D. D. Ruiz, O. Norberto de Souza (auth.), Marie-France Sagot, Maria Emilia M. T. Walter (eds.)
Read Online or Download Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics, BSB 2007, Angra dos Reis, Brazil, August 29-31, 2007. Proceedings PDF
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Additional info for Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics, BSB 2007, Angra dos Reis, Brazil, August 29-31, 2007. Proceedings
As our crossover operator only produces combinations of existing partitions and no mutation is used, the search space will not be explored in details. Thus, the large amount of similar partitions will not be produced by MOCLE, favoring the concision of the set of solutions obtained. Finally, the objective functions should measure the quality of partitions in different ways, each one related to a diﬀerent clustering criterion. They should also Multi-Objective Clustering Ensemble with Prior Knowledge 39 complement each other.
A combinatorial optimization approach for diverse motif ﬁnding applications. Algorithms for Molecular Biology 1–13 (2006) Multi-Objective Clustering Ensemble with Prior Knowledge Katti Faceli1 , Andr´e C. P. L. F. de Carvalho1, and Marc´ılio C. P. br 1 Abstract. In this paper, we introduce an approach to integrate prior knowledge in cluster analysis, which is diﬀerent from the existing ones for semi-supervised clustering methods. In order to aid the discovery of alternative structures present in the data, we consider the knowledge of some existing complete classiﬁcation of such data.
The ﬁrst step in that direction is to compare the most obvious encoding schemes to see which is more appropriate for the problem. One encoding will be to deﬁne an individual as a vector of initial positions for candidate occurrences of the motif. This approach is similar to the one adopted in . The other encoding will deﬁne a chromosome to be the motif itself, resembling other approaches which are not based on the evolutionary paradigm. In the following sections we explain in detail both encodings.