Advances in Data Mining. Applications and Theoretical by Hongmin Cai (auth.), Petra Perner (eds.) PDF

By Hongmin Cai (auth.), Petra Perner (eds.)

ISBN-10: 3642231837

ISBN-13: 9783642231834

ISBN-10: 3642231845

ISBN-13: 9783642231841

This booklet constitutes the refereed lawsuits of the eleventh commercial convention on facts Mining, ICDM 2011, held in big apple, united states in September 2011.

The 22 revised complete papers offered have been rigorously reviewed and chosen from a hundred submissions. The papers are prepared in topical sections on info mining in medication and agriculture, facts mining in advertising, facts mining for business procedures and in telecommunication, Multimedia facts Mining, theoretical points of information mining, info Warehousing, WebMining and data Mining.

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Additional info for Advances in Data Mining. Applications and Theoretical Aspects: 11th Industrial Conference, ICDM 2011, New York, NY, USA, August 30 – September 3, 2011. Proceedings

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N. (1) However, if the possibility exist that the dataset has been contaminated with a small number of anomalies, it might prove beneficial to exclude suspicious points from the sphere and label them as outliers. (1). Obviously, in order to keep the problem non-trivial, one needs to introduce some sort of penalty for the excluded points. (1). More precisely, for each point they introduce a variable ξi ≥ 0 such that xi − a 2 ≤ R2 + ξi . (2) This relaxation of the constraints is then offset by adding a penalty term to the cost function: n ζ(R, a, ξ) := R2 + C ξi .

9), and the linear alternative (in black, cf. eq. (36)). ), has been moved far away from the rest of the cluster. Clearly, the optimal circle based on the linear function is essentially unaffected whereas the SVDD solution is dramatically inflated by this outlier. 4 Conclusions In this paper we re-examined the support vector data descriptor (SVDD) (introduced by [8] and [5]) for one-class classification. Our investigation was prompted by the observation that the definition of slack variables as specified in the SVDD approach, lacks a clear geometric interpretation.

Bobrowski (∀w[n]) ΦR(w[n]) ≥ ΦR(wR*[n]) = ΦR* ≥ 0 (39) The optimal vector wR *[n] (39) defines the ranked model: ŷR = wR*[n]T x[n] . (40) Theorem 2. The minimal value ΦR* = ΦR(wR*[n]) (39) of the criterion function ΦR(w[n]) (38) is equal to zero (ΦR* = 0) and the sets R+ and R- (29) are linearly separable (33) if and only if there exists such weight vector w′[n], that the inequalities w′[n]Txj[n] < w′[n]Txk[n] (8) are fulfilled for each ranked pair {xj[n]½xk[n]} from the learning set C3 (7) ([3], [9]).

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Advances in Data Mining. Applications and Theoretical Aspects: 11th Industrial Conference, ICDM 2011, New York, NY, USA, August 30 – September 3, 2011. Proceedings by Hongmin Cai (auth.), Petra Perner (eds.)


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