Incrementally mining recently repeating patterns over data streams

Jia Ling Koh, Pei Min Chou

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

Abstract

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
Pages26-37
Number of pages12
Volume5433 LNAI
DOIs
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)03029743
ISSN (Electronic)16113349

Other

OtherPacific Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008
CountryJapan
CityOsaka
Period08/5/2009/5/23

Fingerprint

Data Streams
Mining
Sliding Window
Monitoring
Costs
Summarization
Monitor
Closed
Prediction
Experimental Results

Keywords

  • Data streams
  • Incremental mining
  • Repeating patterns

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Koh, J. L., & Chou, P. M. (2009). Incrementally mining recently repeating patterns over data streams. In New Frontiers in Applied Data Mining - PAKDD 2008 International Workshops, Revised Selected Papers (Vol. 5433 LNAI, pp. 26-37). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5433 LNAI). https://doi.org/10.1007/978-3-642-00399-8-3

Incrementally mining recently repeating patterns over data streams. / Koh, Jia Ling; Chou, Pei Min.

New Frontiers in Applied Data Mining - PAKDD 2008 International Workshops, Revised Selected Papers. Vol. 5433 LNAI 2009. p. 26-37 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5433 LNAI).

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

Koh, JL & Chou, PM 2009, Incrementally mining recently repeating patterns over data streams. in New Frontiers in Applied Data Mining - PAKDD 2008 International Workshops, Revised Selected Papers. vol. 5433 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5433 LNAI, pp. 26-37, Pacific Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008, Osaka, Japan, 08/5/20. https://doi.org/10.1007/978-3-642-00399-8-3
Koh JL, Chou PM. Incrementally mining recently repeating patterns over data streams. In New Frontiers in Applied Data Mining - PAKDD 2008 International Workshops, Revised Selected Papers. Vol. 5433 LNAI. 2009. p. 26-37. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-00399-8-3
Koh, Jia Ling ; Chou, Pei Min. / Incrementally mining recently repeating patterns over data streams. New Frontiers in Applied Data Mining - PAKDD 2008 International Workshops, Revised Selected Papers. Vol. 5433 LNAI 2009. pp. 26-37 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{54451d240bfd4d0faf8150654d688eee,
title = "Incrementally mining recently repeating patterns over data streams",
abstract = "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.",
keywords = "Data streams, Incremental mining, Repeating patterns",
author = "Koh, {Jia Ling} and Chou, {Pei Min}",
year = "2009",
doi = "10.1007/978-3-642-00399-8-3",
language = "English",
isbn = "3642003982",
volume = "5433 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "26--37",
booktitle = "New Frontiers in Applied Data Mining - PAKDD 2008 International Workshops, Revised Selected Papers",

}

TY - GEN

T1 - Incrementally mining recently repeating patterns over data streams

AU - Koh, Jia Ling

AU - Chou, Pei Min

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - Data streams

KW - Incremental mining

KW - Repeating patterns

UR - http://www.scopus.com/inward/record.url?scp=67650474316&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=67650474316&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-00399-8-3

DO - 10.1007/978-3-642-00399-8-3

M3 - Conference contribution

AN - SCOPUS:67650474316

SN - 3642003982

SN - 9783642003981

VL - 5433 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 26

EP - 37

BT - New Frontiers in Applied Data Mining - PAKDD 2008 International Workshops, Revised Selected Papers

ER -