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

By Petra Perner

ISBN-10: 3540360360

ISBN-13: 9783540360360

This ebook constitutes the refereed court cases of the sixth business convention on facts Mining, ICDM 2006, held in Leipzig, Germany in July 2006. offers forty five conscientiously reviewed and revised complete papers prepared in topical sections on facts mining in drugs, internet mining and logfile research, theoretical elements of knowledge mining, info mining in advertising and marketing, mining signs and photographs, and facets of information mining, and purposes reminiscent of intrusion detection, and extra.

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Read Online or Download 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 PDF

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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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2. 3. 4. 5. Concatenate a set of homologous genes of a group of organisms into a supergene G . Encoding the super-gene G into a corresponding digit X to be mined by a selforganizing map. Conduct the SOM mining for the numeric matrix X . Computing gene distribution p' ( x ) on the SOM plane for each gene x by retrieving the frequency of sites in the gene hitting their best match unit (BMU) on the SOM plane. Estimating the multispecies gene x entropy by Equation 6. 42 X. g M simply. The super-gene is called a m × n character matrix for the convenience of discussion.

P (10) n=1 Equation (9) can be expressed in matrix form as Ra=r (11) where R is a p × p autocorrelation matrix, r is a p × 1 autocorrelation vector, and a is a p × 1 vector of prediction coefficients: ⎡ ⎤ r(0) r(1) r(2) · · · r(p − 1) ⎢ r(1) r(0) r(1) · · · r(p − 2) ⎥ ⎢ ⎥ ⎢ r(1) r(0) · · · r(p − 3) ⎥ R = ⎢ r(2) ⎥ ⎣ ⎦ · · · ··· · r(p − 1) r(p − 2) r(p − 3) · · · r(0) aT = a1 a2 a3 · · · ap where aT is the tranpose of a, and rT = r(1) r(2) r(3) · · · r(p) where rT is the tranpose of r. Thus, the LPC coefficients can be obtained by solving a = R−1 r (12) where R−1 is the inverse of R.

Table 1. 1263 Apart from this approach to the analysis of biological sequences, the RRM also offers some physical explanation of the selective interactions between biological macromolecules, based on their structure. The RRM considers that these selective interactions (that is the recognition of a target molecule by another molecule, for example, recognition of a promoter by RNA polymerase) are caused by resonant electromagnetic energy exchange, hence the name resonant recognition model. According to the RRM, the charge that is being transferred along the backbone of a macromolecule travels through the changing electric field described by a sequence of EIIPs, causing the radiation of some small amount of electromagnetic energy at particular frequencies that can be recognized by other molecules.

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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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