By Petra Perner
This publication constitutes the refereed lawsuits of the sixth business convention on facts Mining, ICDM 2006, held in Leipzig, Germany in July 2006. provides forty five conscientiously reviewed and revised complete papers geared up in topical sections on information mining in medication, net mining and logfile research, theoretical elements of knowledge mining, info mining in advertising, mining indications and pictures, and facets of knowledge mining, and functions corresponding to 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
Example text
P ∂ak (8) The result is a set of p linear equations p ak r(|m − k|) = r(m), m = 1, . . e. r(−k) = r(k), and expressed as N −m r(m) = s(n) s(n + m), m = 0, . . , 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.
The long-termed sample average can be expressed as [21] 1 n→∞ n n lim D(xi , yi ) (17) i=1 If the vector process is stationary and ergodic, then the limit exists and equals to the expectation of D(xi , yi ). Being analogous to the issue of selecting a particular distance measure for a particular problem, there is no fixed rule for selecting a distortion measure for quantifying the performance of a particular system. In general, an ideal distortion measure should be [21]: 1. Tractable to allow analysis, 2.
3. Vinga,S. : Alignment-free sequence comparisona review. Bioinformatics 19 (2003) 513523. 4. : Ameasure of the similarity of sets of sequences not requiring sequence alignment. Proc. Natl Acad. Sci. USA 83 (1986) 51555159. 5. P. : A measure of DNA sequence dissimilarity based on Mahalanobis distance between frequencies of words. Biometrics 53 (1997) 14311439. 6. C. : Statistical measures of DNA dissimilarity under Markov chain models of base composition. Biometrics 57 (2001) 441448. 7. , Moffett,K.