Approximately mining recently representative patterns on data streams

Jia Ling Koh*, Yuan Bin Don

*Corresponding author for this work

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

3 Citations (Scopus)


Catching the recent trend of data is an important issue when mining frequent itemsets from data streams. To prevent from storing the whole transaction data within the sliding window, the frequency changing point (FCP) method was proposed for monitoring the recent occurrences of itemsets in a data stream under the assumption that exact one transaction arrives at each time point. In this paper, the FCP method is extended for maintaining recent patterns in a data stream where a block of various numbers of transactions (including zero or more transactions) is inputted within each time unit. Moreover, to avoid generating redundant information in the mining results, the recently representative patterns are discovered from the maintained structure approximately. The experimental results show that our approach reduces the run-time memory usage significantly. Moreover, the proposed GFCP algorithm achieves high accuracy of mining results and guarantees no false dismissal occurring.

Original languageEnglish
Title of host publicationEmerging Technologies in Knowledge Discovery and Data Mining - PAKDD 2007 International Workshops, Revised Selected Papers
PublisherSpringer Verlag
Number of pages13
ISBN (Print)354077016X, 9783540770169
Publication statusPublished - 2007
EventPacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007 - Nanjing, China
Duration: 2007 May 222007 May 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4819 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherPacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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