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

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

ISBN-10: 3540717005

ISBN-13: 9783540717003

This ebook 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 awarded 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 learn effects and sensible improvement reviews from all KDD-related parts together with information mining, computer studying, databases, records, information warehousing, facts visualization, computerized clinical discovery, wisdom acquisition and knowledge-based systems.

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Read or Download Advances in Knowledge Discovery and Data Mining: 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007. Proceedings PDF

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Additional 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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Rastogi, K. Shim. ROCK: A Robust Clustering Algorithm for Categorical Attributes. Information Systems 25(5): 345-366, 2000 15. A. A. Keim. An Efficient Approach to Clustering in Large Multimedia Databases with Noise. KDD 1998 16. Z. Huang. Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining & Knowledge Disc. 2(3): 283-304, 1998 17. Arabie. Comparing partitions. Classification 193-218, 1985 18. D. Jiang, J. Pei, A. Zhang. DHC: a density-based hierarchical clustering method for time series gene expression data.

The Entropy is naturally lower (better) in results with many clusters; by comparing results of algorithms with similar numbers of clusters, the HIERDENC Entropy is often lower. The drawback we notice is that the HIERDENC runtime is significantly higher on soybean-data than on zoo. Figure 4 illustrates the HIERDENC tree for zoo. There are 17 leaf clusters in total in the HIERDENC tree. Except for the last 3 created leaf clusters, all other leaf clusters are homogeneous with regards to the class labels of member objects.

Aßfalg et al. description for this representation. Missing representations are a quite common problem in many application areas and thus, should be considered when building a method. Obviously, for each object there has to be at least one oj = ”−”. For a given set of classes C = ci , . . , ck , our classification task can be described in the following way. Given a training set of multi-represented objects T R ⊂ R1 × . . × Rn , we first of all train a classifier CLi : Ri → C for each representation.

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