Approximately mining recently representative patterns on data streams

Jia Ling Koh; Yuan Bin Don

doi:10.1007/978-3-540-77018-3_25

Approximately mining recently representative patterns on data streams

Jia Ling Koh^*, Yuan Bin Don

^*Corresponding author for this work

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Emerging Technologies in Knowledge Discovery and Data Mining - PAKDD 2007 International Workshops, Revised Selected Papers
Publisher	Springer Verlag
Pages	231-243
Number of pages	13
ISBN (Print)	354077016X, 9783540770169
DOIs	https://doi.org/10.1007/978-3-540-77018-3_25
Publication status	Published - 2007
Event	Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007 - Nanjing, China Duration: 2007 May 22 → 2007 May 22

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4819 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007
Country/Territory	China
City	Nanjing
Period	2007/05/22 → 2007/05/22

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-77018-3_25

Cite this

Koh, J. L., & Don, Y. B. (2007). Approximately mining recently representative patterns on data streams. In Emerging Technologies in Knowledge Discovery and Data Mining - PAKDD 2007 International Workshops, Revised Selected Papers (pp. 231-243). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4819 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-77018-3_25

Approximately mining recently representative patterns on data streams. / Koh, Jia Ling; Don, Yuan Bin.
Emerging Technologies in Knowledge Discovery and Data Mining - PAKDD 2007 International Workshops, Revised Selected Papers. Springer Verlag, 2007. p. 231-243 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4819 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Koh, JL & Don, YB 2007, Approximately mining recently representative patterns on data streams. in Emerging Technologies in Knowledge Discovery and Data Mining - PAKDD 2007 International Workshops, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4819 LNAI, Springer Verlag, pp. 231-243, Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, Nanjing, China, 2007/05/22. https://doi.org/10.1007/978-3-540-77018-3_25

Koh JL, Don YB. Approximately mining recently representative patterns on data streams. In Emerging Technologies in Knowledge Discovery and Data Mining - PAKDD 2007 International Workshops, Revised Selected Papers. Springer Verlag. 2007. p. 231-243. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-77018-3_25

Koh, Jia Ling ; Don, Yuan Bin. / Approximately mining recently representative patterns on data streams. Emerging Technologies in Knowledge Discovery and Data Mining - PAKDD 2007 International Workshops, Revised Selected Papers. Springer Verlag, 2007. pp. 231-243 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

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Approximately mining recently representative patterns on data streams

Abstract

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