Incremental updates of closed frequent itemsets over continuous data streams

Hlia-Fu Li*, Chin-Chuan Ho, Suh-Yin Lee

*Corresponding author for this work

研究成果: Article同行評審

54 引文 斯高帕斯(Scopus)


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 (C) 2007 Elsevier Ltd. All rights reserved.
頁(從 - 到)2451-2458
期刊Expert Systems with Applications
出版狀態Published - 三月 2009

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