Advances in Bioinformatics and Computational Biology: Second by K. S. Machado, E. K. Schroeder, D. D. Ruiz, O. Norberto de

By K. S. Machado, E. K. Schroeder, D. D. Ruiz, O. Norberto de Souza (auth.), Marie-France Sagot, Maria Emilia M. T. Walter (eds.)

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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 resources for Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics, BSB 2007, Angra dos Reis, Brazil, August 29-31, 2007. Proceedings

Example text

6 The results in Table 1 show that the motif based algorithm obtains most of the time, for the instance (10, 2), solutions close to the optimum. This means that the motif found at each execution differs from the real motif in one or two bases only. The position based algorithm was not able to find the optimal solution in neither of the 30 executions. In the second instance we can see similar results as in the first one. 30 G. A. Brizuela Table 2. D. D. 52 Table 3. Comparison of average total score (F ) for SGA allowed extra time, the PbGA, and the Gibbs sampler.

For instance, the closet approaches to ours do this by integrating the knowledge through the adaptation of the clustering criterion (or objective function) [7,6]. Demiriz et al. [6], for example, proposed a method to cluster the data whose goal is to generate the purest possible clusters regarding the class distribution. They use a genetic algorithm to minimize a function that is a linear combination of a measure of dispersion of the clusters (unsupervised) and a measure of impurity with respect to the known classes (supervised).

Since we want to restrict the search space to the base partitions and their combination, we do not apply a mutation operator. Therefore, the genetic algorithm aims to select the best partitions, and not to explore all the space of possible partitions. In the pure Pareto based multi-objective clustering scenario, differences in the assignment of only one object to a different cluster in two partitions can result in a different trade-off of the measures optimized. This can result in a high number of very similar partitions in the approximation of the Pareto front obtained.

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