Data Stream Frequent Closed Item Sets Mining Based on Fast Sliding Window

Abstract:

According to the mobility and continuity of the flow of data streams，this paper presents an algorithm called NSWR to mine the frequent item sets from a fast sliding window over data streams and it meets people’s needs of getting the frequent item sets over data that recently arrive. NWSR, using an effective bit-sequence representation of items based on the data stream sliding window, helps to store data; to support different support threshold value inquiry through hash-table-based frequent closed item sets results query method; to offer screening method based on the classification of closed item sets for reducing the number of item sets that need closure judgments, effectively reducing the computational complexity. Experiments show that the algorithm has better time and space efficiency.

• Periodical: Applied Mechanics and Materials (Volumes 130-134)
• Pages: 3702-3707
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.130-134.3702
• Online since: October 2011
• Authors: Zhi Hua Chen, Jun Luo
• Keywords: Data Mining (DM), Data Stream, Frequent Closed Item Sets, Sliding Window

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