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An Efficient Algorithm for Mining Closed Frequent Itemsets in Data Streams

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5 Author(s)
Fujiang Ao ; Sch. of Mech. Eng. & Autom., Nat. Univ. of Defense Technol., Changsha ; Jing Du ; Yuejin Yan ; Baohong Liu
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Mining closed frequent itemsets in the sliding window is one of important topics of data streams mining. In this paper, we propose a novel algorithm, FPCFI-DS, which mines closed frequent itemsets in the sliding window of data streams efficiently, and maintains the precise closed frequent itemsets in the current window at any time. The algorithm uses a single-pass lexicographical-order FP-Tree-based algorithm with mixed item ordering policy to mine the closed frequent itemsets in the first window, and introduces a novel updating approach to process the sliding of window. The experimental results show that FPCFI-DS performs better than the state-of-the-art algorithm Moment in terms of both the time and space efficiencies, especially for dense dataset or low minimum support.

Published in:

Computer and Information Technology Workshops, 2008. CIT Workshops 2008. IEEE 8th International Conference on

Date of Conference:

8-11 July 2008