Finding negative event oriented patterns in long temporal sequences

Sun, Xingzhi, Orlowska, Maria E.. and Li, Xue (2004). Finding negative event oriented patterns in long temporal sequences. In: Honghua Dai, Ramakrishnan Srikant and Chengqi Zhang, Lecture Notes in Computer Science. Advances in Knowledge Discovery and Data Mining: Proceedings of the Eighth Pacific-Asia Conference, PAKDD 2004. The Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2004), Sydney, Autstralia, (212-221). 26-28 May 2004. doi:10.1007/b97861


Author Sun, Xingzhi
Orlowska, Maria E..
Li, Xue
Title of paper Finding negative event oriented patterns in long temporal sequences
Conference name The Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2004)
Conference location Sydney, Autstralia
Conference dates 26-28 May 2004
Proceedings title Lecture Notes in Computer Science. Advances in Knowledge Discovery and Data Mining: Proceedings of the Eighth Pacific-Asia Conference, PAKDD 2004   Check publisher's open access policy
Journal name Advances in Knowledge Discovery and Data Mining, Proceedings   Check publisher's open access policy
Place of Publication Berlin; Heidelberg, Germany
Publisher Springer-Verlag
Publication Year 2004
Sub-type Fully published paper
DOI 10.1007/b97861
ISBN 978-3-540-22064-0
3-540-22064-X
ISSN 0302-9743
1611-3349
Editor Honghua Dai
Ramakrishnan Srikant
Chengqi Zhang
Volume 3056
Start page 212
End page 221
Total pages 10
Collection year 2004
Language eng
Abstract/Summary Pattern discovery in a long temporal event sequence is of great importance in many application domains. Most of the previous work focuses on identifying positive associations among time stamped event types. In this paper, we introduce the problem of defining and discovering negative associations that, as positive rules, may also serve as a source of knowledge discovery. In general, an event-oriented pattern is a pattern that associates with a selected type of event, called a target event. As a counter-part of previous research, we identify patterns that have a negative relationship with the target events. A set of criteria is defined to evaluate the interestingness of patterns associated with such negative relationships. In the process of counting the frequency of a pattern, we propose a new approach, called unique minimal occurrence, which guarantees that the Apriori property holds for all patterns in a long sequence. Based on the interestingness measures, algorithms are proposed to discover potentially interesting patterns for this negative rule problem. Finally, the experiment is made for a real application.
Subjects E1
280108 Database Management
700103 Information processing services
Q-Index Code E1
Additional Notes Subseries: Lecture Notes in Artificial Intelligence; Session 3A: Event Mining, Anomaly Detection, and Intrusion Detection

 
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Created: Thu, 23 Aug 2007, 19:35:23 EST