By Hongmin Cai (auth.), Petra Perner (eds.)
This booklet constitutes the refereed lawsuits of the eleventh business convention on facts Mining, ICDM 2011, held in ny, united states in September 2011.
The 22 revised complete papers provided have been rigorously reviewed and chosen from a hundred submissions. The papers are geared up in topical sections on information mining in medication and agriculture, information mining in advertising and marketing, facts mining for commercial approaches and in telecommunication, Multimedia info Mining, theoretical features of information mining, info Warehousing, WebMining and data Mining.
Read Online 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
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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
By its very deﬁnition, an anomaly is a rare event and training data will more often than not contain very few or even no anomalous exemplars. e. when compared to the majority of regular points. Anomaly detection therefore provides an example of so-called one-class classiﬁcation, the gist of which amounts to the following: Given data points that all originated from a single class but are possibly contaminated with a small number of outliers, ﬁnd the class boundary. al. . The starting point is a classical problem in quadratic P.
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.
Shrinking the tube: A new support vector regression algorithm. Advances in Neural Information Processing Systems (1999) 5. : Support vector method for novelty detection. Advances in Neural Information Processing Systems 12, 582–588 (2000) 6. : Clustering via Minimum Volume Ellipsoids. Journal of Comp. Optimization and App. 37(3) (2007) 7. : A survey of multidimensional medians. International Statistical Review 58(3), 263–277 (1990) 8. : Support vector domain description. Pattern Recognition Letters 20(11-13), 1191–1199 (1999) 9.