estWin: Online data stream mining of recent frequent itemsets by sliding window method

Joong Hyuk Chang, Won Suk Lee

Research output: Contribution to journalArticlepeer-review

67 Citations (Scopus)


Knowledge embedded in a data stream is likely to be changed as time goes by. Identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. However, most mining algorithms over a data stream are not able to extract the recent change of knowledge in a data stream adaptively. This is because the obsolete information of old data elements which may be no longer useful or possibly invalid at present is regarded as being as important as that of recent data elements. This paper proposes a sliding window method that finds recently frequent itemsets over a transactional online data stream adaptively. The size of a sliding window defines the desired life-time of information in a newly generated transaction. Consequently, only recently generated transactions in the range of the window are considered to find the recently frequent itemsets of a data stream.

Original languageEnglish
Pages (from-to)76-90
Number of pages15
JournalJournal of Information Science
Issue number2
Publication statusPublished - 2005

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Library and Information Sciences


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