Mining generalized association rules with quantitative data under multiple support constraints

Authors:
Yeong-Chyi Lee;Tzung-Pei Hong;Chun-Hao Chen
Affiliations:
Department of Information Management, Cheng Shiu University, Taiwan;Department of Science and Information Engineering, National University of Kaohsiung, Taiwan and Department of Computer Science and Engineering, National Sun Yat-sen University, Taiwan;Department of Computer Science and Engineering, Tamkang University, Taiwan
Venue:
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Year:
2010

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Abstract

In this paper, we introduce a fuzzy mining algorithm for discovering generalized association rules with multiple supports of items for extracting implicit knowledge from quantitative transaction data. The proposed algorithm first adopts the fuzzy-set concept to transform quantitative values in transactions into linguistic terms. Besides, each primitive item is given its respective predefined support threshold. The minimum support for an item at a higher taxonomic concept is set as the minimum of the minimum supports of the items belonging to it and the minimum support for an itemset is set as the maximum of the minimum supports of the items contained in the itemset. An example is also given to demonstrate that the proposed mining algorithm can derive the generalized association rules under multiple minimum supports in a simple and effective way.