By Hongmin Cai (auth.), Petra Perner (eds.)
This ebook 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 awarded have been conscientiously reviewed and chosen from a hundred submissions. The papers are geared up in topical sections on info mining in medication and agriculture, information mining in advertising, facts mining for commercial strategies and in telecommunication, Multimedia info Mining, theoretical elements 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
T. ∀i = 1, . . , n : xi − a 2 ≤ R2 + ξi , ξi ≥ 0. (3) i=1 The resulting data summarization segregates “regular” points on the inside from “outliers” on the outside of the sphere and is called support vector data description (SVDD). Aim of this paper. (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.
In addition, we introduce a vector ξ = (ξ1 , . . , ξn ) of n slack variables in terms of which we can deﬁne the cost function ζ(a, R, ξ) := R2 + C ξi . t. ∀i = 1, . . , n : xi − a 2 ≤ R2 + ξi , ξi ≥ 0. (9) If we denote the distance of each point xi to the centre a as di = xi − a then it’s straightforward to see that the slack variables can be expliciﬁed as ξi := (d2i − R2 )+ , where the ramp function x+ is deﬁned by: x+ := x if x ≥ 0, 0 if x < 0, (10) This allows us to rewrite the cost function in a more concise form: (d2i − R2 )+ .
The last row shows the number of samples assigned to the class shown in row 1 and the last line shows the real class distribution. Based on this table, we can calculate parameters that assess the quality of the classifier. How to Interpret Decision Trees? 47 Table 2. Contingency Table Assigned Class Index Real Class Index 1 i c11 ... … … c1i cii … … ... cji ... cm1 ... 1 … i … j ... m Sum ... … … … ... ... m c1m … c1m … ... cmm Sum The correctness p is the number of correct classified samples over the number of samples: m p= ∑c i =1 m m ii ∑∑ c i =1 j =1 (4) ji For the investigation of the classification quality we measure the classification quality pki according to a particular class i and the number of correct classified samples pti for one class i: pki cii pti = m ∑c j =1 ji cii m ∑c i =1 (5) ji Other criteria shown in Table 3 are also important when judging the quality of a model.