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29

HICSS
2007
IEEE

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Mining Fuzzy Weighted Association Rules

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Mining Fuzzy Weighted Association Rules

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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.

David L. Olson, Yanhong Li

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Association Rules | Biometrics | Fuzzy Association Rules | Fuzzy Data Mining | HICSS 2007 | System Sciences |

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Added	02 Jun 2010
Updated	02 Jun 2010
Type	Conference
Year	2007
Where	HICSS
Authors	David L. Olson, Yanhong Li

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