Efficient mining method for retrieving sequential patterns over online data streams

Joong Hyuk Chang, Won Suk Lee

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


With the usefulness of data mining in various fields of information science, various mining methods have been proposed in previous research. Recently, in these fields, data has taken the form of continuous data streams rather than finite stored data sets. In this paper, a mining method of sequential patterns over an online sequence data stream is proposed, which is useful for retrieving embedded knowledge in the data stream. The proposed method can minimize memory usage of the mining process while an error is allowed in its mining result, and supports flexible trade-off between memory usage and mining accuracy. However, the error is minimized by an accurate estimation method for the count of a sequence, which considers the ordering information of items. The proposed method can catch a recent change in a sequence data stream in a short time, by a decaying mechanism gracefully discarding old information that may be no longer useful.

Original languageEnglish
Pages (from-to)420-432
Number of pages13
JournalJournal of Information Science
Issue number5
Publication statusPublished - 2005

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Library and Information Sciences


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