DTGC-TREE: A NEW STRATEGY OF ASSOCIATION RULES MINING

摘要：

Efficient algorithms for mining frequent itemsets are crucial for mining association rules. As a condensed and complete representation of all the frequent itemsets, closed frequent itemsets mining has arisen a lot of interests in the data mining community. However, most of the studies havent addressed the effects of noise in the data sets on the algorithms, and there has been limited attention to the development of noise tolerant algorithms.In this paper, we represent a noise tolerant algorithm, DTGC-Tree, which based on an intuitive idea: applying association rules as soon as possible. By this way, the new algorithm could prune a lot of duplicated closed itemsets in the transactional data base. The performance evaluation demonstrates that the proposed algorithm could stand against noise and is both time and space efficient.

关键词： Frequent Itemsets Closed Itemsets Association Rule Data Mining

作者: JUN-BO CHEN BO ZHOU YIQUN DING LU CHEN

作者单位: Department of computer science, Zhejiang University, Hangzhou, Zhejinag, China State Street Corporation, Hangzhou, Zhejiang, China

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: 245-250

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）