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 commercial convention on facts Mining, ICDM 2011, held in ny, 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 information mining in drugs and agriculture, information mining in advertising and marketing, information mining for commercial tactics and in telecommunication, Multimedia information Mining, theoretical elements 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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Acknowledgement. This research is partially supported by the Specific Targeted Research Project (STReP) FireSense Fire Detection and Management through a Multi-Sensor Network for the Protection of Cultural Heritage Areas from the Risk of Fire and Extreme Weather Conditions (FP7-ENV-20091244088-FIRESENSE) of the European Union’s 7th Framework Programme Environment (including Climate Change). References 1. : Support vector clustering. Journal of Machine Learning Research 2, 125–137 (2001) 2. : An improved cluster labeling method for support vector clustering.

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. Indeed, in Section 2 we’ve already argued that eq. (29) implies that the sphere’s centre is the (weighted) mean of the support vectors. Noticing that in eq. (39) the vectors have been substituted by the corresponding unit vectors reveals that in the case of a linear loss function the centre can be thought of as the weighted median of the support vectors.

1 where the bottom figure plots the piecewise constant coefficient β for two different values of C, while the corresponding ζ functions are plotted in the top graph. Clearly, every β-plateau gives rise to a different quadratic part of ζ. More importantly, as long as β(R) < 0 the resulting quadratic part in ζ is strictly decreasing. Hence we conclude that the minimum of ζ occurs at the point R∗ = arg min ζ(R) where β jumps above zero. Indeed, at that point, the corresponding quadratic becomes strictly increasing, forcing the minimum to be located at the jump between the two segments.

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