19 July 2013 A fuzzy approach for mining association rules in a probabilistic database
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Proceedings Volume 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013); 88781G (2013) https://doi.org/10.1117/12.2031777
Event: Fifth International Conference on Digital Image Processing, 2013, Beijing, China
Abstract
Association rule mining is an essential knowledge discovery method that can find associations in database. Previous studies on association rule mining focus on finding quantitative association rules from certain data, or finding Boolean association rules from uncertain data. Unfortunately, due to instrument errors, imprecise of sensor monitoring systems and so on, real-world data tend to be quantitative data with inherent uncertainty. In our paper, we study the discovery of association rules from probabilistic database with quantitative attributes. Once we convert quantitative attributes into fuzzy sets, we get a probabilistic database with fuzzy sets in the database. This is theoretical challenging, since we need to give appropriate interest measures to define support and confidence degree of fuzzy events with probability. We propose a Shannon-like Entropy to measure the information of such event. After that, an algorithm is proposed to find fuzzy association rules from probabilistic database. Finally, an illustrated example is given to demonstrate the procedure of the algorithm.
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Bin Pei, Dingjie Chen, Suyun Zhao, Hong Chen, "A fuzzy approach for mining association rules in a probabilistic database", Proc. SPIE 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013), 88781G (19 July 2013); doi: 10.1117/12.2031777; https://doi.org/10.1117/12.2031777
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