Advances in Data Mining. Applications in Medicine, Web by Rainer Schmidt, Tina Waligora (auth.), Petra Perner (eds.)

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

The commercial convention on info Mining ICDM-Leipzig used to be the 6th occasion in a chain of annual occasions which began in 2000. we're happy to notice that the subject info mining with precise emphasis on real-world purposes has been followed via such a lot of researchers around the globe into their examine paintings. We obtained 156 papers from 19 diversified international locations. the most subject matters are facts mining in drugs and advertising, net mining, mining of pictures and signs, theoretical features of information mining, and elements of information mining that package a sequence of alternative information mining functions comparable to intrusion detection, wisdom administration, production method keep watch over, time-series mining and felony investigations. this system Committee labored demanding so one can opt for the easiest papers. The reputation fee used to be 30%. these kind of 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 provided. those papers are gathered in a distinct poster lawsuits quantity and convey once again the potentials and engaging advancements of information mining for various functions. 3 new workshops were tested in reference to ICDM: (1) Mass information research on photographs and signs, MDA 2006; (2) facts Mining for all times Sciences, DMLS 2006; and (3) info Mining in advertising, DMM 2006. those workshops are constructing new issues for info mining less than the element of the designated program. we're happy to determine what number attention-grabbing advancements are happening in those fields.

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Extra 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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The application of vector quantization (VQ) [21] of LPC coefficients, where the distance measure is the distance between two LPC vectors, can be a potential approach for improving the calculation of similarity. This can also be readily extended to the use of VQ-based hidden Markov models [19] for similarity searching. References 1. J. : Statistical Methods in Bioinformatics. Springer, NY, 2001. 2. : Comparison of genomic DNA sequences: solved and unsolved problems. Bioinformatics 17 (2001) 391397.

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. 17. : Genomic signal processing, IEEE Signal Processing Magazine 18 (2001) 8-20.

Obviously, the reference matrix W is a k × m dimensional matrix. After the SOM finishes learning, the reference vector matrix W stores prototypes of the original input data matrix X that is the extracted feature-data of the input dataset. The unsupervised learning algorithm l takes the winner-take-all-rule in the learning. It consists of loops of competition, cooperation and adjusting in addition to the initialization stage at the beginning of the selforganizing learning. The time complexity of each epoch in the SOM learning is O(nmk ) , where k is the number of neurons on the SOM plane and n × m is the dimension of the input dataset X .

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