Abstract
In this paper, a new effective method is proposed to find class association rules (CAR), to getuseful class association rules (UCAR) by removing thespurious class association rules (SCAR), and to generateexception class association rules (ECAR) for each UCAR. CAR mining, which integrates the techniques of classification and association, is of great interest recently. However, it has two drawbacks: one is that a large part of CARs are spurious and may be misleading to users; the other is that some important ECARs are difficult to find using traditional data mining techniques. The method introduced in this paper aims to get over these flaws. According to our approach, a user can retrieve correct information from UCARs and know the influence from different conditions by checking corresponding ECARs. Experimental results demonstrate the effectiveness of our proposed approach.
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This work is supported by the National Natural Science Foundation of China under grant No.60003016 and the NKBRSF of China under grant No.G1998030404.
ZHOU Aoying received his M.S. degree in computer science from Sichuan University in 1988, and his Ph.D. degree in computer software from Fudan University in 1993. He is currently a professor in the Department of Computer Science, Fudan University. His main research interests include object-oriented data models for multimedia information, Web data management, data mining and data warehousing, novel database technologies and their application in digital library and electronic commerce.
WEL Li is a graduate student of the Computer Science Department, Fudan University. Her research interests include data mining and text compression.
YU Fang was an undergraduate student at Fudan University while this work was performed. She is currently with the Department of Computer Science, University of California, Los Angeles, CA, USA. Her research interests include knowledge-base system and distributed database system.
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Zhou, A., Wei, L. & Yu, F. Effective discovery of exception class association rules. J. of Comput. Sci. & Technol. 17, 304–313 (2002). https://doi.org/10.1007/BF02947308
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DOI: https://doi.org/10.1007/BF02947308