Mining Fuzzy Weighted Association Rules

Authors:
David L. Olson;Yanhong Li
Affiliations:
University of Nebraska, Lincoln, NE;Shanghai University Finance, Shanghai, China
Venue:
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
Year:
2007

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Abstract

The paper combines and extends the technologies of fuzzy sets and association rules, considering users' differential emphasis on each attribute through fuzzy regions. A fuzzy data mining algorithm is proposed to discovery fuzzy association rules for weighted quantitative data. This is expected to be more realistic and practical than crisp association rules. Discovered rules are expressed in natural language that is more understandable to humans. The paper demonstrates the performance of the proposed approach using a synthetic but realistic dataset.