Abstract
This paper defines a new kind of rule, probability functional dependency rule. The functional dependency degree can be depicted by this kind of rule. Five algorithms, from the simple to the complex, are presented to mine this kind of rule in different condition. The related theorems are proved to ensure the high efficiency and the correctness of the above algorithms.
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Supported by the National natural Science Foundation of China and the National Doctoral Subject Foundation, National 973 Fundamental Research Program, the project 863-306-04-04-2, and the project 863-306-04-03-5.
TAO Xiaopeng received his M.S. degree in computer science from Peking University in 1995. He is currently a Ph.D. candidate in the Department of Computer Science, Fudan University. His main research interests include full-text database, text retrieval, KDD, text mining.
ZHOU Aoying received his M.S. degree in computer science from Chengdu University of Science and Technology 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 model for multimedia information, CIMS data management, data mining and data warehousing, the novel database technologies and their application in digital library and electronic commerce.
HU Yunfa graduated from Fudan University in 1964. He is currently a Professor, Permanent Member of Council of Chinese Artificial Intelligence Society and Vice-director of Shanghai (International) research center. The areas of his research cover database, knowledge base and AI.
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Tao, X., Zhou, A. & Hu, Y. Fast algorithms of mining probability functional dependency rules in relational database. J. Comput. Sci. & Technol. 15, 261–270 (2000). https://doi.org/10.1007/BF02948813
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DOI: https://doi.org/10.1007/BF02948813