Read e-book online Advances in Data Mining: Applications in Medicine, Web PDF

By Petra Perner

ISBN-10: 3540360360

ISBN-13: 9783540360360

This e-book constitutes the refereed complaints of the sixth commercial convention on facts Mining, ICDM 2006, held in Leipzig, Germany in July 2006. offers forty five conscientiously reviewed and revised complete papers equipped in topical sections on information mining in medication, internet mining and logfile research, theoretical elements of knowledge mining, information mining in advertising, mining indications and pictures, and elements of knowledge mining, and purposes reminiscent of intrusion detection, and extra.

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Additional info for Advances in Data Mining: Applications in Medicine, Web Mining, Marketing, Image and Signal Mining: 6th Industrial Conference on Data Mining, ICDM 2006, Leipzig, Germany, July 2006, Proceedings

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105-108. 13. : Macromolecular bioactivity: Is it resonant interaction between macromolecules? – theory and applications, IEEE trans. Biomedical Engineering 41 (1994) 1101-1114. 14. , Dimitrijevic, B. : Is it possible to analyze DNA and protein sequences by the methods of digital signal processing? IEEE Trans. Biomed. Eng. 32 (1985) 337-341. 15. H. de Trad, Q. Fang, and I. Cosic, Protein sequence comparison based on the wavelet transform approach, Protein Engineering 15 (2002) 193-203. 16. : Frequency-domain analysis of biomolecular sequences, Bioinformatics 16 (2000) 1073-1082.

There are 45 congruent genes and 61 incongruent genes from Bayesian analysis [21] under GTR+Γ model [13,20]. Such classification result of ours is confirmed by the Shimodaira-Hasegawa test (SH test) [22] under the GTR+Γ model with reestimated log likelihoods (RELL) approximation for 21 tree topologies for each gene (bootstrap replicates: 1000). A gene is called a phylogenetically informative gene if its corresponding phylogenetic tree is nearest to the species tree in the robustness test (for example, KH, SH test [13,20,22]).

9 for the SRBC, MLL and AML/ALL datasets). 2 0 2 4 6 8 10 12 14 K Fig. 5. Optimal value of DDP, α*, plotted against K for all benchmark datasets The steeper decrease of α* as K increases in the SMA implies that the measure of relevance used in the SMA fails to capture the relevance of a feature when K is large. In the OVA scheme, the decrease of α* as K increases is more gradual, implying better effectiveness than the SMA in capturing relevance for datasets with larger K. Furthermore, for all datasets, the value of α* in the OVA scheme is greater than or equal to the value of α* in the SMA.

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Advances in Data Mining: Applications in Medicine, Web Mining, Marketing, Image and Signal Mining: 6th Industrial Conference on Data Mining, ICDM 2006, Leipzig, Germany, July 2006, Proceedings by Petra Perner


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