Combining clustering with moving sequential pattern mining: A novel and efficient technique

Shuai Ma; Shiwei Tang; Dongqing Yang; Tengjiao Wang; Jinqiang Han

doi:10.1007/978-3-540-24775-3_51

Combining clustering with moving sequential pattern mining: A novel and efficient technique

Shuai Ma
, Shiwei Tang
, Dongqing Yang
, Tengjiao Wang
, Jinqiang Han

Peking University

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

8 Scopus citations

Abstract

Sequential pattern mining is a well-studied problem. In the context of mobile computing, moving sequential patterns that reflects the moving behavior of mobile users attracted researchers’ interests recently. In this paper a novel and efficient technique is proposed to mine moving sequential patterns. Firstly the idea of clustering is introduced to process the original moving histories into moving sequences as a preprocessing step. Then an efficient algorithm called PrefixTree is presented to mine the moving sequences. Performance study shows that PrefixTree outperforms LM algorithm, which is revised to mine moving sequences, in mining large moving sequence databases.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings
Editors	Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang
Publisher	Springer Verlag
Pages	419-423
Number of pages	5
ISBN (Print)	354022064X, 9783540220640
DOIs	https://doi.org/10.1007/978-3-540-24775-3_51
State	Published - 2004
Externally published	Yes
Event	8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004 - Sydney, Australia Duration: 26 May 2004 → 28 May 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3056
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004
Country/Territory	Australia
City	Sydney
Period	26/05/04 → 28/05/04

Access to Document

10.1007/978-3-540-24775-3_51

Cite this

Ma, S., Tang, S., Yang, D., Wang, T., & Han, J. (2004). Combining clustering with moving sequential pattern mining: A novel and efficient technique. In H. Dai, R. Srikant, & C. Zhang (Eds.), Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings (pp. 419-423). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3056). Springer Verlag. https://doi.org/10.1007/978-3-540-24775-3_51

Ma, Shuai ; Tang, Shiwei ; Yang, Dongqing et al. / Combining clustering with moving sequential pattern mining : A novel and efficient technique. Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. editor / Honghua Dai ; Ramakrishnan Srikant ; Chengqi Zhang. Springer Verlag, 2004. pp. 419-423 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Combining clustering with moving sequential pattern mining: A novel and efficient technique",

abstract = "Sequential pattern mining is a well-studied problem. In the context of mobile computing, moving sequential patterns that reflects the moving behavior of mobile users attracted researchers{\textquoteright} interests recently. In this paper a novel and efficient technique is proposed to mine moving sequential patterns. Firstly the idea of clustering is introduced to process the original moving histories into moving sequences as a preprocessing step. Then an efficient algorithm called PrefixTree is presented to mine the moving sequences. Performance study shows that PrefixTree outperforms LM algorithm, which is revised to mine moving sequences, in mining large moving sequence databases.",

author = "Shuai Ma and Shiwei Tang and Dongqing Yang and Tengjiao Wang and Jinqiang Han",

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year = "2004",

doi = "10.1007/978-3-540-24775-3\_51",

language = "英语",

isbn = "354022064X",

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

publisher = "Springer Verlag",

pages = "419--423",

editor = "Honghua Dai and Ramakrishnan Srikant and Chengqi Zhang",

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}

Ma, S, Tang, S, Yang, D, Wang, T & Han, J 2004, Combining clustering with moving sequential pattern mining: A novel and efficient technique. in H Dai, R Srikant & C Zhang (eds), Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3056, Springer Verlag, pp. 419-423, 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004, Sydney, Australia, 26/05/04. https://doi.org/10.1007/978-3-540-24775-3_51

Combining clustering with moving sequential pattern mining: A novel and efficient technique. / Ma, Shuai; Tang, Shiwei; Yang, Dongqing et al.
Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. ed. / Honghua Dai; Ramakrishnan Srikant; Chengqi Zhang. Springer Verlag, 2004. p. 419-423 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3056).

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

TY - GEN

T1 - Combining clustering with moving sequential pattern mining

T2 - 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004

AU - Ma, Shuai

AU - Tang, Shiwei

AU - Yang, Dongqing

AU - Wang, Tengjiao

AU - Han, Jinqiang

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2004.

PY - 2004

Y1 - 2004

N2 - Sequential pattern mining is a well-studied problem. In the context of mobile computing, moving sequential patterns that reflects the moving behavior of mobile users attracted researchers’ interests recently. In this paper a novel and efficient technique is proposed to mine moving sequential patterns. Firstly the idea of clustering is introduced to process the original moving histories into moving sequences as a preprocessing step. Then an efficient algorithm called PrefixTree is presented to mine the moving sequences. Performance study shows that PrefixTree outperforms LM algorithm, which is revised to mine moving sequences, in mining large moving sequence databases.

AB - Sequential pattern mining is a well-studied problem. In the context of mobile computing, moving sequential patterns that reflects the moving behavior of mobile users attracted researchers’ interests recently. In this paper a novel and efficient technique is proposed to mine moving sequential patterns. Firstly the idea of clustering is introduced to process the original moving histories into moving sequences as a preprocessing step. Then an efficient algorithm called PrefixTree is presented to mine the moving sequences. Performance study shows that PrefixTree outperforms LM algorithm, which is revised to mine moving sequences, in mining large moving sequence databases.

UR - https://www.scopus.com/pages/publications/7444270747

U2 - 10.1007/978-3-540-24775-3_51

DO - 10.1007/978-3-540-24775-3_51

M3 - 会议稿件

AN - SCOPUS:7444270747

SN - 354022064X

SN - 9783540220640

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

SP - 419

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BT - Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings

A2 - Dai, Honghua

A2 - Srikant, Ramakrishnan

A2 - Zhang, Chengqi

PB - Springer Verlag

Y2 - 26 May 2004 through 28 May 2004

ER -

Ma S, Tang S, Yang D, Wang T, Han J. Combining clustering with moving sequential pattern mining: A novel and efficient technique. In Dai H, Srikant R, Zhang C, editors, Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. Springer Verlag. 2004. p. 419-423. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-24775-3_51

Combining clustering with moving sequential pattern mining: A novel and efficient technique

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