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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Extra info for Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics, BSB 2007, Angra dos Reis, Brazil, August 29-31, 2007. Proceedings
18] and to the ones produced by Waterman et al. . In  an algorithm based on random projections of the motif is proposed. Constructed under the planted motif model, this algorithm obtains a good performance compared with the commonly used methods like Gibbs  and MEME . In  the Motif Finding Problem is deﬁned as to ﬁnd a local alignment of multiple sequences without gaps using the sum-of-pairs scoring scheme and the ﬁlogenetic distance. Combinatorial techniques like branch pruning and linear programming are used to solve the problem.
All this were made to use the data in the same way as in its original paper. For all datasets, we generate the initial population with the algorithms kmeans (KM), average-link (AL), single-link (SL)  and Shared Nearest Neighbors (SNN) . These algorithms generate diﬀerent types of clusters. KM and LM looks for compact clusters and SL and SNN obtain connected clusters. KM, LM and LS were chosen because they are traditional and largely employed clustering algorithms . In its turns, SNN is a recent technique and was selected because it can robustly deal with high dimensionality, noise and outliers .
As already mentioned, MOCLE relies on the ability of the clustering algorithms in ﬁnding high quality partitions according to the employed criteria. Starting with a set of potentially good partitions, MOCLE uses the multiple objectives to select the best compromises. New partitions are created only by means of the crossover operator and represent the consensus among other existing partitions. As our crossover operator only produces combinations of existing partitions and no mutation is used, the search space will not be explored in details.