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Natl. Acad. Sci. USA 95 (1998) 14863–14868 35. : Click: A clustering algorithm for gene expression analysis. In: 8th International Conference on Intelligent Systems for Molecular Biology (ISMB 00), AAAI Press (2000) 36. : Systematic determination of genetic network architecture. Nature Genetics volume 22 (1999) 281–285 37. : Unsupervised clustering in mRNA expression profiles. Computers in Biology and Medicine 36(10) (2006) 38. : Largescale temporal gene expression mapping of cns development. Proceedings of the National Academy of Science USA 95 (1998) 334–339 39.

Natural Computing 1 (2002) 307–322 23. : Clustering in evolutionary algorithms to efficiently compute simultaneously local and global minima. In: IEEE Congress on Evolutionary Computation. , Edinburgh, UK (2005) 1847–1854 24. : Irrelevant features and the subset selection problem. In: International Conference on Machine Learning. (1994) 121–129 25. : Fast algorithms for projected clustering. In: 1999 ACM SIGMOD international conference on Management of data, ACM Press (1999) 61–72 26. : Automatic subspace clustering of high dimensional data for data mining applications.

Although, the FNNs trained using the PCA projection of the data set, in general, provide high classification accuracy, there is no straightforward way to select the right number of factors for each problem. K. Tasoulis et al. 5%, for the optimal selection of the number of factors. The above discussion suggests that the UkW algorithm is capable of automatically identifying meaningful groups of features, while the PCA technique optimally transforms the data set, with limited loss of information, to a space of significantly lower dimension.

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A p-version finite element method for nonlinear elliptic variational inequalities in 2D by Krebs A., Stephan E.P.


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