Download e-book for iPad: 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.)

ISBN-10: 3540717005

ISBN-13: 9783540717003

ISBN-10: 3540717013

ISBN-13: 9783540717010

This ebook constitutes the refereed complaints of the eleventh Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2007, held in Nanjing, China in may well 2007.

The 34 revised complete papers and ninety two revised brief papers provided including 4 keynote talks or prolonged abstracts thereof have been rigorously reviewed and chosen from 730 submissions. The papers are dedicated to new principles, unique examine effects and functional improvement stories from all KDD-related components together with info mining, computing device studying, databases, information, information warehousing, facts visualization, automated medical 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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Extra info for Advances in Knowledge Discovery and Data Mining: 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007. Proceedings

Sample text

HIERDENC offers a basis for designing simpler clustering algorithms that balance the tradeoff of accuracy and speed. The characteristics of HIERDENC include: (i) it builds a hierarchy representing the underlying cluster structure of the categorical dataset, (ii) it minimizes the user-specified input parameters, (iii) it is insensitive to the order of object input, (iv) it can handle outliers. We evaluate HIERDENC on small-dimensional standard categorical datasets, on which it produces more accurate results than other algorithms.

Each of these subtrees is stored in a priority queue which is ordered by the minimum distance of o to any node of the subtree. e. o is classified), we can process the priority queue containing the collected subtrees. If the first object in the queue is a leaf node, we determine whether the leaf corresponds to a class which is different to the class prediction. In this case, we can calculate CRange(o) as the distance of o to the boundaries of this leaf. Since the priority queue is ordered by the minimum distance to o, there cannot exist any other leaf with a boundary closer to o than the calculated CRange(o).

Andreopoulos. Clustering Algorithms for Categorical Data. PhD Thesis, Dept of Computer Science & Engineering, York University, Toronto, Canada, 2006 4. M. Ankerst, M. P. Kriegel, J. Sander. OPTICS: Ordering Points to Identify the Clustering Structure. SIGMOD 1999 5. D. Barbara, Y. Li, J. Couto. COOLCAT:an entropy-based algorithm for categorical clustering. CIKM 2002 6. P. Berkhin. Survey of Clustering Data Mining Techniques. Accrue Software, Inc. TR, San Jose, USA, 2002 7. Z. N. L. Barabasi. Bioinformatics analysis of experimentally determined protein complexes in the yeast Saccharomyces cerevisiae.

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Advances in Knowledge Discovery and Data Mining: 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007. Proceedings by Jiawei Han (auth.), Zhi-Hua Zhou, Hang Li, Qiang Yang (eds.)


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