Finding event-oriented patterns in long temporal sequences

Xingzhi Sun; Maria E. Orlowska; Xiaofang Zhou

doi:10.1007/3-540-36175-8_3

Finding event-oriented patterns in long temporal sequences

Xingzhi Sun, Maria E. Orlowska, Xiaofang Zhou

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

13 Citations (Scopus)

Abstract

A major task of traditional temporal event sequence mining is to find all frequent event patterns from a long temporal sequence. In many real applications, however, events are often grouped into different types, and not all types are of equal importance. In this paper, we consider the problem of efficient mining of temporal event sequences which lead to an instance of a specific type of event. Temporal constraints are used to ensure sensibility of the mining results. We will first generalise and formalise the problem of event-oriented temporal sequence data mining. After discussing some unique issues in this new problem, we give a set of criteria, which are adapted from traditional data mining techniques, to measure the quality of patterns to be discovered. Finally we present an algorithm to discover potentially interesting patterns.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining
Editors	Kyu-Young Wang, Jongwoo Jeon, Kyuseok Shim, Jaideep Srivastava
Publisher	Springer Verlag
Pages	15-26
Number of pages	12
ISBN (Electronic)	3540047603, 9783540047605
DOIs	https://doi.org/10.1007/3-540-36175-8_3
Publication status	Published - 2003
Externally published	Yes
Event	7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 - Seoul, Korea, Republic of Duration: 30 Apr 2003 → 2 May 2003

Publication series

Name	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	2637
ISSN (Print)	0302-9743

Conference

Conference	7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003
Country/Territory	Korea, Republic of
City	Seoul
Period	30/04/03 → 2/05/03

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2003.

Access to Document

10.1007/3-540-36175-8_3

Cite this

Sun, X., Orlowska, M. E., & Zhou, X. (2003). Finding event-oriented patterns in long temporal sequences. In K.-Y. Wang, J. Jeon, K. Shim, & J. Srivastava (Eds.), Advances in Knowledge Discovery and Data Mining (pp. 15-26). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2637). Springer Verlag. https://doi.org/10.1007/3-540-36175-8_3

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title = "Finding event-oriented patterns in long temporal sequences",

abstract = "A major task of traditional temporal event sequence mining is to find all frequent event patterns from a long temporal sequence. In many real applications, however, events are often grouped into different types, and not all types are of equal importance. In this paper, we consider the problem of efficient mining of temporal event sequences which lead to an instance of a specific type of event. Temporal constraints are used to ensure sensibility of the mining results. We will first generalise and formalise the problem of event-oriented temporal sequence data mining. After discussing some unique issues in this new problem, we give a set of criteria, which are adapted from traditional data mining techniques, to measure the quality of patterns to be discovered. Finally we present an algorithm to discover potentially interesting patterns.",

author = "Xingzhi Sun and Orlowska, \{Maria E.\} and Xiaofang Zhou",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2003.; 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 ; Conference date: 30-04-2003 Through 02-05-2003",

year = "2003",

doi = "10.1007/3-540-36175-8\_3",

language = "English",

series = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",

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booktitle = "Advances in Knowledge Discovery and Data Mining",

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Sun, X, Orlowska, ME & Zhou, X 2003, Finding event-oriented patterns in long temporal sequences. in K-Y Wang, J Jeon, K Shim & J Srivastava (eds), Advances in Knowledge Discovery and Data Mining. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), vol. 2637, Springer Verlag, pp. 15-26, 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003, Seoul, Korea, Republic of, 30/04/03. https://doi.org/10.1007/3-540-36175-8_3

Finding event-oriented patterns in long temporal sequences. / Sun, Xingzhi; Orlowska, Maria E.; Zhou, Xiaofang.
Advances in Knowledge Discovery and Data Mining. ed. / Kyu-Young Wang; Jongwoo Jeon; Kyuseok Shim; Jaideep Srivastava. Springer Verlag, 2003. p. 15-26 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2637).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

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AB - A major task of traditional temporal event sequence mining is to find all frequent event patterns from a long temporal sequence. In many real applications, however, events are often grouped into different types, and not all types are of equal importance. In this paper, we consider the problem of efficient mining of temporal event sequences which lead to an instance of a specific type of event. Temporal constraints are used to ensure sensibility of the mining results. We will first generalise and formalise the problem of event-oriented temporal sequence data mining. After discussing some unique issues in this new problem, we give a set of criteria, which are adapted from traditional data mining techniques, to measure the quality of patterns to be discovered. Finally we present an algorithm to discover potentially interesting patterns.

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T3 - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

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Finding event-oriented patterns in long temporal sequences

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