Incrementally mining recently repeating patterns over data streams

Jia Ling Koh, Pei Min Chou

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Repeating patterns represent temporal relations among data items, which could be used for data summarization and data prediction. More and more data of various applications is generated as a data stream. Based on time sensitive concern, mining repeating patterns from the whole history data sequence of a data stream does not extract the current trend of patterns over the stream. Therefore, the traditional strategies for mining repeating patterns on static database are not applicable to data streams. For this reason, an algorithm, named appearing-bit-sequence-based incremental mining algorithm, for efficiently discovering recently repeating patterns over a data stream is proposed in this paper. The appearing bit sequences are used to monitor the occurrences of patterns within a sliding window. Two versions of algorithms are proposed by maintaining the appearing bit sequences of maximum repeating patterns and closed repeating patterns, respectively. Accordingly, the cost of re-mining repeating patterns over a sliding window is reduced to that of monitoring frequency changes of the maintained patterns. The experimental results show that the incremental mining methods perform much better than the re-miming approach.

Original languageEnglish
Title of host publicationNew Frontiers in Applied Data Mining - PAKDD 2008 International Workshops, Revised Selected Papers
PublisherSpringer Verlag
Number of pages12
ISBN (Print)3642003982, 9783642003981
Publication statusPublished - 2009
EventPacific Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008 - Osaka, Japan
Duration: 2008 May 202009 May 23

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5433 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherPacific Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008


  • Data streams
  • Incremental mining
  • Repeating patterns

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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