TWO REVISED ALGORITHMS BASED ON APRIORI FOR MINING ASSOCIATION RULES

摘要：

Association rule mining is concerned with the discovery of interesting association relationships hidden in databases. Traditional algorithms are only considering the constraints of minimum support and minimum confidence. However, sometimes it is essential to find stronger association rules for decision makers possessing inadequate resources, and sometimes less strong rules are needed. In this paper, we propose two revised algorithms based on Apriori considering the constraints of three factors: minimum support, minimum confidence and minimum interest. In order to reduce the times of scanning a database, we adopt a matrix structure in our algorithms.

关键词： Data mining Association rule Matriz

作者: WEI-MIN MA ZHU-PING LIU

作者单位: School of Economics and Management, Tongji University, Shanghai 200092, China School of Economics an School of Economics and Management, Beijing University of Aeronautics and Astronautics, Beijing 1000

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 350-355

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）