Mining

Download Advanced Data Mining and Applications: 7th International by Yong-Bin Kang, Shonali Krishnaswamy (auth.), Jie Tang, Irwin PDF

By Yong-Bin Kang, Shonali Krishnaswamy (auth.), Jie Tang, Irwin King, Ling Chen, Jianyong Wang (eds.)

The two-volume set LNAI 7120 and LNAI 7121 constitutes the refereed complaints of the seventh overseas convention on complex information Mining and purposes, ADMA 2011, held in Beijing, China, in December 2011. The 35 revised complete papers and 29 brief papers awarded including three keynote speeches have been rigorously reviewed and chosen from 191 submissions. The papers conceal a variety of subject matters featuring unique learn findings in info mining, spanning functions, algorithms, software program and platforms, and utilized disciplines.

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Additional info for Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part I

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ICDT 1999. LNCS, vol. 1540, pp. 398–416. Springer, Heidelberg (1998) 26. : On Characterization and Discovery of Minimal Unexpected Patterns in Rule Discovery. IEEE Transactions on Knowledge and Data Engineering 18(2), 202–216 (2006) 27. : A Regression-Based Temporal Pattern Mining Scheme for Data Streams. In: Proc. VLDB (2003) 28. : Weighted Association Rule Mining using Weighted Support and Significance Framework. In: Proc. SIGKDD (2003) 29. : estMax: Tracing Maximal Frequent Itemsets over Online Data Streams.

Li and N. Zhang Definition 4(Actual Maximal Frequent Itemset). If an itemset X is an actual maximal frequent itemset, and it is covered by possible frequent itemsets, infrequent itemsets or none itemsets, it is called an actual maximal frequent itemset(AMF ). Definition 5(Shifty Un-Maximal Frequent Itemset). If an itemset X is a shifty frequent itemset and covered by shifty frequent itemsets, it is called a shifty un-maximal frequent itemset(SUMF ). Definition 6(Shifty Maximal Frequent Itemset). If an itemset X is a shifty frequent itemset, and it is covered by possible frequent itemsets, infrequent itemsets, or none itemsets, it is called a shifty maximal frequent itemset(SMF ).

ACM Press (1998) 14. : Nouvelles recherches sur la distribution florale. Bulletin de la Societe Vaudoise de Sciences Naturelles 44, 223–370 (1908) 15. : Algorithms for clustering data. Prentice-Hall, Inc. (1988) 16. : Data clustering: A review. ACM Computing Survey 31(3), 264–323 (1999) 17. : Finding Groups in Data An Introduction to Cluster Analysis. Wiley Interscience (1990) 18. : Enumerating all connected maximal common subgraphs in two graphs. Theoretical Computer Science 250(1-2), 1–30 (2001) 19.

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