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

This publication constitutes the refereed lawsuits of the eleventh business convention on information Mining, ICDM 2011, held in big apple, united states in September 2011.

The 22 revised complete papers provided have been conscientiously reviewed and chosen from a hundred submissions. The papers are geared up in topical sections on facts mining in medication and agriculture, facts mining in advertising, information mining for commercial strategies and in telecommunication, Multimedia info 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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E. set-valued) (24) ⎪ ⎩ 0 if x < 0 To forestall confusion we point out that, unlike when used as a distribution, this definition of the Heaviside function insists its value at the origin is between zero and one. Using this convention, we have the convenient shorthand notation: ∂x+ = H(x). Computing the subgradients (for convenience we will drop the notational distinction between standard- and sub-gradients) we obtain: ∂ζ = 2R − 2RC ∂R ∇a ζ = −2C H(d2i − R2 ) (25) i H(d2i − R2 ) (xi − a) (26) i where we used the well-known fact: ∇a (d2i ) = ∇a ||xi − a||2 = ∇a (xi · xi − 2xi · a + a · a) = −2(xi − a).

People from different geographic areas and different nations can access a website and leave a distinct track dependent on the geographic area they are from and the nation they belong to. If the user has to label the data, then it might be apparent that the subjective decision about the class the data set belongs to might result in some noise. Depending on the form of the day of the expert or on his experience level, he will label the data properly or not as well as he should. Oracle-based classification methods [12][13] or similaritybased methods [14][15] might help the user to overcome such subjective factors.

28) whereas eq. (29). In more detail: 1. Once again we can make the distinction between the nin points that reside inside the sphere, the non points that lie on the sphere and the nout points that are outside the sphere. The latter two categories constitute the support vectors: #SV = non + nout . Hence, 1/C = hi = i hi + di R θi + nout . di =R So also in this case we get (cf. eq. (35)): nout ≤ 1 ≤ #SV. C (40) 2. Comparing eqs. (39) and (29) we conclude that we can expect the solution corresponding to linear loss function (36) to be more robust with respect to outliers.

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