An approximate approach for mining recently frequent itemsets from data streams

Jia Ling Koh, Shu Ning Shin

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

11 Citations (Scopus)

Abstract

Recently, the data stream, which is an unbounded sequence of data elements generated at a rapid rate, provides a dynamic environment for collecting data sources. It is likely that the embedded knowledge in a data stream will change quickly as time goes by. Therefore, catching the recent trend of data is an important issue when mining frequent itemsets from data streams. Although the sliding window model proposed a good solution for this problem, the appearing information of the patterns within the sliding window has to be maintained completely in the traditional approach. In this paper, for estimating the approximate supports of patterns within the current sliding window, two data structures are proposed to maintain the average time stamps and frequency changing points of patterns, respectively. The experiment results show that our approach will reduce the run-time memory usage significantly. Moreover, the proposed FCP algorithm achieves high accuracy of mining results and guarantees no false dismissal occurring.

Original languageEnglish
Title of host publicationData Warehousing and Knowledge Discovery - 8th International Conference, DaWaK 2006, Proceedings
PublisherSpringer Verlag
Pages352-362
Number of pages11
ISBN (Print)3540377360, 9783540377368
Publication statusPublished - 2006 Jan 1
Event8th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2006 - Krakow, Poland
Duration: 2006 Sep 42006 Sep 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4081 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2006
CountryPoland
CityKrakow
Period06/9/406/9/8

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Koh, J. L., & Shin, S. N. (2006). An approximate approach for mining recently frequent itemsets from data streams. In Data Warehousing and Knowledge Discovery - 8th International Conference, DaWaK 2006, Proceedings (pp. 352-362). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4081 LNCS). Springer Verlag.