A New Algorithm for Maintaining Closed Frequent Itemsets in Data Streams by Incremental Updates

Authors:
Hua-Fu Li;Chin-Chuan Ho;Fang-Fei Kuo;Suh-Yin Lee
Affiliations:
National Chiao Tung University, Taiwan;National Chiao Tung University, Taiwan;National Chiao Tung University, Taiwan;National Chiao Tung University, Taiwan
Venue:
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Year:
2006

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Abstract

Online mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we propose an efficient one-pass algorithm, NewMoment to maintain the set of closed frequent itemsets in data streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and memory needed to slide the windows. Experiments show that the proposed algorithm not only attain highly accurate mining results, but also run significant faster and consume less memory than existing algorithm Moment for mining closed frequent itemsets over recent data streams.