Moment+: Mining Closed Frequent Itemsets over Data Stream

Authors:
Haifeng Li;Hong Chen
Affiliations:
Key Laboratory of Data Engineering and Knowledge Engineering, MOE, School of Information, Renmin University of China, Beijing, China 100872;Key Laboratory of Data Engineering and Knowledge Engineering, MOE, School of Information, Renmin University of China, Beijing, China 100872
Venue:
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Year:
2008

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Abstract

Closed frequent itemsets(CFI) mining uses less memory to store the entire information of frequent itemsets thus is much suitable for mining stream. In this paper, we discuss recent CFImining methods over stream and presents an improved algorithm Moment+based on the existent one Moment. Moment+focuses on the problem of mining CFIover data stream sliding window and proposes a new structure Extended Closed Enumeration Tree(ECET) to store the CFIsand nodes' BPNwhich is introduced to reduce the search space, with which new mining method is designed to mine more rapidly with little memory cost sacrifice. The experimental results show that this method is effective and efficient.