Mining sequential rules common to several sequences with the window size constraint

Philippe Fournier-Viger; Cheng Wei Wu; S. Tseng; Roger Nkambou

doi:10.1007/978-3-642-30353-1_27

Mining sequential rules common to several sequences with the window size constraint

Philippe Fournier-Viger^*, Cheng Wei Wu, S. Tseng, Roger Nkambou

^*Corresponding author for this work

Institute of Data Science and Engineering

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

23 Scopus citations

Abstract

We present an algorithm for mining sequential rules common to several sequences, such that rules have to appear within a maximum time span. Experimental results with real-life datasets show that the algorithm can reduce the execution time, memory usage and the number of rules generated by several orders of magnitude compared to previous algorithms.

Original language	English
Title of host publication	Advances in Artificial Intelligence - 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Proceedings
Pages	299-304
Number of pages	6
DOIs	https://doi.org/10.1007/978-3-642-30353-1_27
State	Published - 2012
Event	25th Canadian Conference on Artificial Intelligence, AI 2012 - Toronto, ON, Canada Duration: 28 May 2012 → 30 May 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7310 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th Canadian Conference on Artificial Intelligence, AI 2012
Country/Territory	Canada
City	Toronto, ON
Period	28/05/12 → 30/05/12

Keywords

sequence database
sequential rules
sliding-window
time

Access to Document

10.1007/978-3-642-30353-1_27

Cite this

Fournier-Viger, P., Wu, C. W., Tseng, S., & Nkambou, R. (2012). Mining sequential rules common to several sequences with the window size constraint. In Advances in Artificial Intelligence - 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Proceedings (pp. 299-304). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7310 LNAI). https://doi.org/10.1007/978-3-642-30353-1_27

Fournier-Viger, Philippe ; Wu, Cheng Wei ; Tseng, S. et al. / Mining sequential rules common to several sequences with the window size constraint. Advances in Artificial Intelligence - 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Proceedings. 2012. pp. 299-304 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{0b2828806a8a4b979f44fede4190ba29,

title = "Mining sequential rules common to several sequences with the window size constraint",

abstract = "We present an algorithm for mining sequential rules common to several sequences, such that rules have to appear within a maximum time span. Experimental results with real-life datasets show that the algorithm can reduce the execution time, memory usage and the number of rules generated by several orders of magnitude compared to previous algorithms.",

keywords = "sequence database, sequential rules, sliding-window, time",

author = "Philippe Fournier-Viger and Wu, {Cheng Wei} and S. Tseng and Roger Nkambou",

year = "2012",

doi = "10.1007/978-3-642-30353-1_27",

language = "English",

isbn = "9783642303524",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "299--304",

booktitle = "Advances in Artificial Intelligence - 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Proceedings",

note = "25th Canadian Conference on Artificial Intelligence, AI 2012 ; Conference date: 28-05-2012 Through 30-05-2012",

}

Fournier-Viger, P, Wu, CW, Tseng, S & Nkambou, R 2012, Mining sequential rules common to several sequences with the window size constraint. in Advances in Artificial Intelligence - 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7310 LNAI, pp. 299-304, 25th Canadian Conference on Artificial Intelligence, AI 2012, Toronto, ON, Canada, 28/05/12. https://doi.org/10.1007/978-3-642-30353-1_27

Mining sequential rules common to several sequences with the window size constraint. / Fournier-Viger, Philippe; Wu, Cheng Wei; Tseng, S. et al.
Advances in Artificial Intelligence - 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Proceedings. 2012. p. 299-304 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7310 LNAI).

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

TY - GEN

T1 - Mining sequential rules common to several sequences with the window size constraint

AU - Fournier-Viger, Philippe

AU - Wu, Cheng Wei

AU - Tseng, S.

AU - Nkambou, Roger

PY - 2012

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N2 - We present an algorithm for mining sequential rules common to several sequences, such that rules have to appear within a maximum time span. Experimental results with real-life datasets show that the algorithm can reduce the execution time, memory usage and the number of rules generated by several orders of magnitude compared to previous algorithms.

AB - We present an algorithm for mining sequential rules common to several sequences, such that rules have to appear within a maximum time span. Experimental results with real-life datasets show that the algorithm can reduce the execution time, memory usage and the number of rules generated by several orders of magnitude compared to previous algorithms.

KW - sequence database

KW - sequential rules

KW - sliding-window

KW - time

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U2 - 10.1007/978-3-642-30353-1_27

DO - 10.1007/978-3-642-30353-1_27

M3 - Conference contribution

AN - SCOPUS:84861735455

SN - 9783642303524

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

SP - 299

EP - 304

BT - Advances in Artificial Intelligence - 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Proceedings

T2 - 25th Canadian Conference on Artificial Intelligence, AI 2012

Y2 - 28 May 2012 through 30 May 2012

ER -

Fournier-Viger P, Wu CW, Tseng S, Nkambou R. Mining sequential rules common to several sequences with the window size constraint. In Advances in Artificial Intelligence - 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Proceedings. 2012. p. 299-304. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-30353-1_27

Mining sequential rules common to several sequences with the window size constraint

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Keywords

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