By Rainer Schmidt, Tina Waligora (auth.), Petra Perner (eds.)

The commercial convention on info Mining ICDM-Leipzig was once the 6th occasion in a chain of annual occasions which begun in 2000. we're happy to notice that the subject information mining with targeted emphasis on real-world functions has been followed via such a lot of researchers world wide into their examine paintings. We acquired 156 papers from 19 diverse international locations. the most themes are info mining in drugs and advertising, internet mining, mining of pictures and indications, theoretical features of knowledge mining, and elements of information mining that package deal a sequence of alternative information mining functions similar to intrusion detection, wisdom administration, production procedure keep watch over, time-series mining and felony investigations. this system Committee labored tough so that it will decide on the simplest papers. The popularity price was once 30%. most of these chosen papers are released during this lawsuits quantity as lengthy papers as much as 15 pages. additionally we put in a discussion board the place paintings in growth was once offered. those papers are accumulated in a unique poster complaints quantity and express once again the potentials and engaging advancements of information mining for various functions. 3 new workshops were proven in reference to ICDM: (1) Mass information research on photographs and indications, MDA 2006; (2) info Mining for all times Sciences, DMLS 2006; and (3) facts Mining in advertising and marketing, DMM 2006. those workshops are constructing new themes for information mining below the point of the targeted software. we're happy to work out what number attention-grabbing advancements are happening in those fields.

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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 14-15, 2006. Proceedings

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After that, for the purpose of extracting mutual spectral characteristics of sequences, having the same or similar biological function, cross-spectral function is used: Sn = Xn Yn∗ n = 1, 2, . . , N 2 (3) where Xn is the DFT coefficients of the xm , Yn∗ is the complex conjugate DFT coefficients of the y(m). Based on the above cross-spectral function, we can obtain a spectrum. In the spectrum, peak frequencies, which are assumed that mutual spectral frequency of two analyzed sequences, can be observed [13].

Acad. Sci. 98(24) (2001) 13784–13789 3. : Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270 (1995) 467–470 4. : A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Research 6(7) (1996) 639–645 5. : Redundancy Based Feature Selection for Microarray Data. In: Proc. 2004 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2004) 737–742 6. : Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method.

Gov/Entrez). In addition, we compared all six sequences with a randomly generated sequence (rand-thrA), using the same length and base composition as ec-thrA. To compare our proposed technique with other methods, we calculated the sequence similarity or sequence distance using alignment-based methods. All seven sequences have been aligned using CLUSTALW [24]. The multiple sequence alignment has then been used to calculate an identity matrix and the distance matrix using DNADist from the PHYLIP package [25] and the modification of the Kimura distance model [26].

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