Advances in Knowledge Discovery and Data Mining: 11th by Jiawei Han (auth.), Zhi-Hua Zhou, Hang Li, Qiang Yang (eds.)

By Jiawei Han (auth.), Zhi-Hua Zhou, Hang Li, Qiang Yang (eds.)

This publication constitutes the refereed court cases of the eleventh Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2007, held in Nanjing, China in may well 2007.

The 34 revised complete papers and ninety two revised brief papers offered including 4 keynote talks or prolonged abstracts thereof have been conscientiously reviewed and chosen from 730 submissions. The papers are dedicated to new principles, unique study effects and useful improvement studies from all KDD-related components together with info mining, desktop studying, databases, information, facts warehousing, info visualization, computerized clinical discovery, wisdom acquisition and knowledge-based systems.

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Extra info for Advances in Knowledge Discovery and Data Mining: 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007. Proceedings

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The general methodology used is the multi-classifier strategy. In the multi-classifier system, each classifier has its own classification criterion and input feature set. Firstly, the strategy is used to optimize the combination of the results from different , ∗ Supported by the National Natural Science Foundation of China (Grant No. 60321002), the National Key Foundation R&D Project (Grant No. 2003CB317007, 2004CB318108). -H. Zhou, H. Li, and Q. ): PAKDD 2007, LNAI 4426, pp. 9–10, 2007. © Springer-Verlag Berlin Heidelberg 2007 10 B.

1] based on a second training set T Rconf for each classifier which predicts the confidence of the class decision CLi (oi ) for each object oi . Let us note that we employed cross validation for this second training since the number of training objects is usually limited. To combine these results, we employ the following combination method: CLglobal (o) = CLargmax {CECLj (o) } (o) 0≤j≤n where o is an unknown data object. In other words, we employ each classifier CLj (o) for deriving a class and afterwards determine the confidence of this class decision CECLj (o).

Moreover, it has recently been shown that the “curse of dimensionality” involving efficient searches for approximate nearest neighbors in a metric space can be dealt with, if and only if, we assume a bounded dimensionality [12,21]. Clearly, there are tradeoffs of efficiency and approximation involved in the design of categorical clustering algorithms. Ideally, a set of probabilistically justified goals for categorical clustering would serve as a framework for approximation algorithms [20,25]. This would allow designing and comparing categorical clustering algorithms on a more formal basis.

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