UF-EVOLVE - UNCERTAIN FREQUENT PATTERN MINING

摘要：

Many frequent-pattern mining algorithms were designed to handle precise data, such as the FP-tree structure and the FP-growth algorithm. In data mining research, attention has been turned to mining frequent patterns in uncertain data recently. We want frequent-pattern mining algorithms for handling uncertain data. A common way to represent the uncertainty of a data item in record databases is to associate it with an existential probability. In this paper, we propose a novel uncertain-frequent-pattern discover structure, the mUF-tree, for storing summarized and uncertain information about frequent patterns. With the mUF-tree, the UF-Evolve algorithm can utilize the shuffling and merging techniques to generate iterative versions of it. Our main purpose is to discover new uncertain frequent patterns from iterative versions of the mUF-tree. Our preliminary performance study shows that the UF-Evolve algorithm is efficient and scalable for mining additional uncertain frequent patterns with different sizes of uncertain databases.

关键词： Uncertain frequent pattern mining Tree Shuffling and merging

作者: Shu Wang Vincent Ng

作者单位: Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

会议类型: 国际会议

会议名称: 13th International Conference on Enterprise Information System(第13届企业信息系统国际会议 ICEIS 2011)

会议地点: 北京

会议语种:英文

页码: 2463-2473

在线出版日期: 2011-06-08（万方平台首次上网日期，不代表论文的发表时间）