Algorithms for mining association rules in bag databases

Authors:
Ping-Yu Hsu;Yen-Liang Chen;Chun-Ching Ling
Affiliations:
Department of Business Administration, National Central University, Chung-Li 320, Taiwan, ROC;Department of Information Management, National Central University, Chung-Li 320, Taiwan, ROC;Department of Information Management, National Central University, Chung-Li 320, Taiwan, ROC
Venue:
Information Sciences—Informatics and Computer Science: An International Journal
Year:
2004

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Abstract

Existing studies in mining association rules in transaction databases assume that a transaction only records the items bought in that particular transaction. However, a typical transaction also records the quantities of items. Because quantity information is not incorporated in the analysis, the association rules cannot reveal what quantities of different items are related with one another. Therefore, this paper reconsiders the conventional transaction database by assuming that each transaction is formed of a set of items as well as their quantities. (We name this extended transaction database as bag database.) In bag databases, algorithms are developed for mining association rules including items' quantities, and three kinds of association rules are generated.