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Finding Interesting Associations without Support Pruning

Cohen, E. and Datar, M. and Fujiwara, S. and Gionis, A. and Indyk, P. and Motwani, R. and Ullman, J. and Yang, C. (2000) Finding Interesting Associations without Support Pruning. In: 16th International Conference on Data Engineering (ICDE 2000), February 28 - March 3, 2000, San Diego, California.




Association-rule mining has heretofore relied on the condition of high support to do its work efficiently. In particular, the well-known a-priori algorithm is only effective when the only rules of interest are relationships that occur very frequently. However, there are a number of applications, such as data mining, identification of similar web documents, clustering, and collaborative filtering, where the rules of interest have comparatively few instances in the data. In these cases, we must look for highly correlated items, or possibly even causal relationships between infrequent items. We develop a family of algorithms for solving this problem, employing a combination of random sampling and hashing techniques. We provide analysis of the algorithms developed, and conduct experiments on real and synthetic data to obtain a comparative performance analysis

Item Type:Conference or Workshop Item (Paper)
Subjects:Computer Science > Data Mining
Projects:Information Integration
Related URLs:Project Homepage
ID Code:485
Deposited By:Import Account
Deposited On:25 Feb 2000 16:00
Last Modified:27 Dec 2008 11:55

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