Incremental updates of closed frequent itemsets over continuous data streams

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


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.
Original languageEnglish
Pages (from-to)2451-2458
JournalExpert Systems with Applications
Issue number2
StatePublished - Mar 2009


  • Data mining; Data streams; Closed frequent itemsets; Single-pass mining; Incremental update

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