By David L. Olson, Desheng Wu (auth.), Xue Li, Shuliang Wang, Zhao Yang Dong (eds.)

This ebook constitutes the refereed lawsuits of the 1st overseas convention on complex facts Mining and functions, ADMA 2005, held in Wuhan, China in July 2005.

The convention was once all for subtle concepts and instruments that may deal with new fields of knowledge mining, e.g. spatial facts mining, biomedical info mining, and mining on high-speed and time-variant information streams; a ramification of information mining to new purposes is usually strived for. The 25 revised complete papers and seventy five revised brief papers provided have been rigorously peer-reviewed and chosen from over six hundred submissions. The papers are prepared in topical sections on organization principles, class, clustering, novel algorithms, textual content mining, multimedia mining, sequential info mining and time sequence mining, net mining, biomedical mining, complex functions, defense and privateness concerns, spatial facts mining, and streaming info mining.

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Additional resources for Advanced Data Mining and Applications: First International Conference, ADMA 2005, Wuhan, China, July 22-24, 2005. Proceedings

Example text

9] introduced a novel system to mine topic-specific knowledge on the Web. The intuition is that the frequent word phrases in a collection of web pages of the same topic are most likely to be the sub-topics or the salient concepts. We can find out several different methods and system for clustering transactions [16] and documents [2, 6]. These are all based on the intuition that there should be many frequent itemsets within a cluster and different clusters have little overlapping of such frequent itemsets.

For example, consider an alphabet of 5 letters {a, b, c, d, e}. e 25 = 32). All these combinations are shown in Figure 1. These 5 letters could be the 5 unique products that a specialized store sells. The combinations illustrated in Figure 1 are the possible transactions that potential customers have when visiting this store. To study the relationships between products bought together, one might take existing real transactions and for each transaction, check the combinations, and for each of them traverse the graph in Figure 1 to increment the respective counters.

R. Za¨ıane 6. M. El-Hajj and O. R. Za¨ıane. Cofi approach for mining frequent itemsets revisited. In 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD-04), pages 70–75, Paris, France, June 2004. 7. D. Gusfield. Algorithms on Strings, Trees, and sequences: Computer Science and Coputational Biology. Cambridge University Press, 1997. 8. B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. In 4th Intl. Conf. on Knowledge Discovery and Data Mining (KDD’98), pages 80–86, New York City, NY, August 1998.

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