A sliding window method for finding recently frequent itemsets over online data streams

Joong Hyuk Chang, Won Suk Lee

Research output: Contribution to journalArticle

110 Citations (Scopus)

Abstract

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is likely to be changed as time goes by. However, most of mining algorithms or frequency approximation algorithms for a data stream do not able to extract the recent change of information in a data stream adaptively. This paper proposes a sliding window method of finding recently frequent itemsets over an online data stream. The size of a window defines a desired life-time of the information of a transaction in a data stream.

Original languageEnglish
Pages (from-to)753-762
Number of pages10
JournalJournal of Information Science and Engineering
Volume20
Issue number4
Publication statusPublished - 2004 Jul 1

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Hardware and Architecture
  • Library and Information Sciences
  • Computational Theory and Mathematics

Cite this