Journal
INFORMATION SCIENCES
Volume 237, Issue -, Pages 242-260Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2013.02.010
Keywords
Fuzzy association rule; Probabilistic quantitative database; Fuzzy-probabilistic database; Shannon-like Entropy
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Funding
- National Basic Research Program of China (973 program) [2012CB316205]
- National Natural Science Foundation of China [61070056, 61033010, 61202114]
- HGJ Important National Science & Technology Specific Projects of China [2010ZX01042-001-002-002]
- Fundamental Research Funds of Renmin University of China [12XNLF07]
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Current studies on association rule mining focus on finding Boolean/quantitative association rules from certain databases or Boolean association rules from probabilistic databases. However, little work on mining association rules from probabilistic quantitative databases has been mentioned because the simultaneous measurement of quantitative information and probability is difficult. By introducing a novel Shannon-like Entropy, we aggregate and measure the information contained in an item with the coexistence of fuzzy uncertainty hidden in quantitative values and random uncertainty. We then propose Support and Confidence metrics for a fuzzy-probabilistic database to quantify association rules. Finally, we design an algorithm, called FARP (mining Fuzzy Association Rules from a Probabilistic quantitative data), to discover frequent fuzzy-probabilistic itemsets and fuzzy 'association rules using the proposed interest measures. The experimental results show the effectiveness of our method and its practicality in real-world applications. (C) 2013 Elsevier Inc. All rights reserved.
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