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

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

This booklet constitutes the refereed complaints of the eleventh business convention on facts Mining, ICDM 2011, held in manhattan, united states in September 2011.

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

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Extra resources 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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We therefore conclude that for values C ≥ 1 there will be no outliers. This result was also obtained in [8,5] but we can now further tighten the above bound by observing that when the optimal sphere encloses all points, it has to pass through at least two points (irrespective of the ambient Support Vector Data Description Revisited 35 dimension). This implies that dn−1 = dn and the first non-trivial interval preceding dn is in fact [dn−2 , dn−1 ]. Rerunning the above analysis, we can conclude that C ≥ 1/2 implies that all data points are enclosed.

Mroczek, T. ) Human-Computer Systems Interaction. Backgrounds and Applications. Advances in Soft Computing, vol. 2. Springer, Heidelberg (in the press, 2011) 7. : Selection of high risk patients with ranked models based on the CPL criterion functions. In: Perner, P. ) ICDM 2010. LNCS, vol. 6171, pp. 432–441. Springer, Heidelberg (2010) 8. : Eksploracja danych oparta na wypukłych i odcinkowo-liniowych funkcjach kryterialnych, Data mining based on convex and piecewise linear criterion functions, Technical University Białystok (2005) (in Polish) 9.

1. Top: Total cost ζ (for two slightly different values of the unit cost C) as a function of the radius R for a simple data set comprising four points. This continuous function is composed of quadratic segments β(R)R2 + Cγ(R). The piecewise constant behaviour of the β coefficient (which determines whether the segment is increasing or decreasing) is plotted in the bottom figure. Bottom: The quadratic coefficient β(R) = 1 − C i bi (R) is a piecewise constant function for which the jumps occur whenever R equals one of the distances di = xi − a .

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