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.

Show description

Read or Download Advances in Data Mining. Applications and Theoretical Aspects: 11th Industrial Conference, ICDM 2011, New York, NY, USA, August 30 – September 3, 2011. Proceedings PDF

Similar industrial books

Industrial Catalysis: A Practical Approach, Second Edition

Even though greater than ninety% of construction methods in are catalyzed, such a lot chemists and engineers are constrained to trial and mistake while looking for the right kind catalyst. This ebook is the 1st emphasizing business elements of catalysis and in addition really compatible to learning on one's personal.

Industrial and Environmental Xenobiotics: Metabolism and Pharmacokinetics of Organic Chemicals and Metals Proceedings of an International Conference held in Prague, Czechoslovakia, 27’30 May 1980

The e-book you're simply going to learn represents the larger a part of the papers awarded on the overseas convention on business and En­ vironmental Xenobiotics, held in Prague, 1980, and a few contributions through those that couldn't come. the 1st objective of the assembly was once to fol­ low the culture arrange via the 1st convention in 1977.

Industrial Cooperation between East and West

Booklet by way of Levcik, Friedrich

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

Sample text

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.

Download PDF sample

Rated 4.28 of 5 – based on 45 votes