By Hongmin Cai (auth.), Petra Perner (eds.)
This e-book constitutes the refereed court cases 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 rigorously reviewed and chosen from a hundred submissions. The papers are equipped in topical sections on facts mining in medication and agriculture, information mining in advertising, information mining for business approaches and in telecommunication, Multimedia info Mining, theoretical elements of information mining, info Warehousing, WebMining and data Mining.
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Extra 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
3) lack a straightforward geometrical interpretation. Indeed, denoting di = xi − a , it transpires that the slack variables can be represented explicitly as: ξi = (d2i − R2 )+ = d2i − R2 if di > R, 0 if di ≤ R. (4) However, except in the case where the dimension of the ambient space (p) equals two or three, these slack variables don’t have an obvious geometric interpretation. It would therefore be more natural to set the slack variable equal to ϕi = (di − R)+ upon which the relaxed constraints can be expressed as: ∀i : xi − a ≤ R + ϕi , ϕi ≥ 0.
The improved robustness of the solution of the linear cost function (36) with respect to the original one (7) is nicely illustrated in Fig. 2. 2 Further Properties To gain further insight in the behaviour of solutions we once again assume that the centre of the sphere has already been located, so that the cost function depends solely on R. We also assume that the points have been labeled to produce an increasing sequence of distances di = xi − a . (16) Rearranging the terms we arrive at: ζ1 (R) = 1 − C bi (R) R + C bi (R)di , (41) i which elucidates that the function is piecewise linear, with a piecewise constant slope equal to 1 − C bi .
Different methods can be applied to accomplish this. While in the past mostly black box methods such as neural nets and support vector machines (SVM) have been heavily used in technical domains, methods that have explanation capability have been particularly used in medical domains since a physician likes to understand the outcome of a classifier and map it to his domain knowledge; otherwise, the level of acceptance of an automatic system is low. Nowadays, data mining methods with explanation capability are also used for technical domains after more work on advantages and disadvantages of the methods has been done.