Algorithm Research for mining mazimal frequent itemsets Based on Item Constraints

摘要：

Frequent item mining has been extensively used in association rules mining. The goal of frequent itemset mining is to discover all the itemsets whose supports in the database exceed a user-specified threshold. However, it often generates a large number of candidate itemset,which reduce the effectiveness of the mining algorithms. Constraint-based mining enables users to provide restraints on mining their interested association rules and can greatly improve the efficiency of mining tasks. In this paper, we propose a fast constraint-based algorithm for mining maximal frequent itemsets.The algorithm introduces item-constraints into the Eclat algorithm, and adopts itemset extension pruning strategy to prun search space. Empirical evaluation showed that the algorithM is very effective and can solve the lack of constrained frequent itemsets algorithm in mining long pattern and intensive database.

关键词： Association rules Mazimal frequent itemsets constrained-based mining

作者: Sang lin Hu-yan cui Ren ying Zhou-lin lin

作者单位: Department of Mathematics Dalian Maritime University Dalian Liaoning 116026

会议类型: 国际会议

会议名称: Second International Symposium on Information Science and Engineering(第二届信息科学与工程国际会议)

会议地点: 上海

会议语种:英文

页码: 629-633

在线出版日期: 2009-12-26（万方平台首次上网日期，不代表论文的发表时间）