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 e-book constitutes the refereed court cases of the eleventh commercial convention on info Mining, ICDM 2011, held in big apple, united states in September 2011.

The 22 revised complete papers provided have been rigorously reviewed and chosen from a hundred submissions. The papers are prepared in topical sections on info mining in drugs and agriculture, information mining in advertising and marketing, information mining for business techniques and in telecommunication, Multimedia facts Mining, theoretical features of information mining, information 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

Example text

Anomaly detection therefore provides an example of so-called one-class classification, the gist of which amounts to the following: Given data points that all originated from a single class but are possibly contaminated with a small number of outliers, find the class boundary. al. [4]. The starting point is a classical problem in quadratic P. ): ICDM 2011, LNAI 6870, pp. 25–39, 2011. J. Pauwels and O. Ambekar programming: given a set of n points x1 , . . , xn in a p-dimensional space, find the most tightly fitting (hyper)sphere that encompasses all.

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.

It is particularly important, when the number n of variables (features) Xi is high in comparison to the number m of objects Oj. Typically such situation occurs in the case of bioinformatics data sets. Acknowledgment. This work was supported by the by the NCBiR project N R13 0014 04, and partially financed by the project S/WI/2/2011 from the Białystok University of Technology, and by the project 16/St/2011 from the Institute of Biocybernetics and Biomedical Engineering PAS. References 1. : Applied Multivariate Statistical Analysis.

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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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