Mining multiple-level association rules under the maximum constraint of multiple minimum supports

Authors:
Yeong-Chyi Lee;Tzung-Pei Hong;Tien-Chin Wang
Affiliations:
Department of Information Engineering, I-Shou University, Kaohsiung, Taiwan;Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung, Taiwan;Department of Information Management, I-Shou University, Kaohsiung, Taiwan
Venue:
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Year:
2006

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Abstract

In this paper, we propose a multiple-level mining algorithm for discovering association rules from a transaction database with multiple supports of items. Items may have different minimum supports and taxonomic relationships, and the maximum-itemset minimum-taxonomy support constraint is adopted in finding large itemsets. That is, the minimum support for an itemset is set as the maximum of the minimum supports of the items contained in the itemset, while the minimum support of the item at a higher taxonomic concept is set as the minimum of the minimum supports of the items belonging to it. Under the constraint, the characteristic of downward-closure is kept, such that the original Apriori algorithm can easily be extended to find large itemsets. The proposed algorithm adopts a top-down progressively deepening approach to derive large itemsets. An example is also given to demonstrate that the proposed mining algorithm can proceed in a simple and effective way.