By Jiawei Han (auth.), Zhi-Hua Zhou, Hang Li, Qiang Yang (eds.)
This booklet constitutes the refereed lawsuits of the eleventh Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2007, held in Nanjing, China in may possibly 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 useful improvement reports from all KDD-related parts together with information mining, computing device studying, databases, facts, facts warehousing, information visualization, automated medical discovery, wisdom acquisition and knowledge-based systems.
Read Online 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 resources for Advances in Knowledge Discovery and Data Mining: 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007. Proceedings
On the other hand, a coarse representation of one modality, for example the global feature of an image such as color moment, color correlagram and global texture has a good robustness but a poor expressiveness. Contrarily, its fine representation, a representation with small grain-size, such as a pixel-based representation of an image has a good expressiveness but a poor robustness. Both expressiveness and robustness are needed in machine representation. Therefore, the multi-granular representation in one modality may solve the contradiction among them.
MULIC aims not to lose cluster structure caused by several large clusters being merged during the clustering process. Computational Complexity. The best-case complexity of MULIC has a lower bound of Ω(mN k) and its worst-case complexity has an upper bound ). The cost is related to the number of clusters k and the of O(mN 2 threshold δφ number of objects N . Often k N, m N , and all objects are clustered in the initial iterations, thus N often dominates the cost. The worst-case runtime would occur for the rather uncommon dataset where all objects were extremely dissimilar to one another, such that the algorithm had to go through all m iterations and all N objects were clustered in the last iteration when φ = m.
Data Mining and Knowledge Discovery 6: 303-360, 2002 14. S. Guha, R. 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.
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.)