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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wang, J.T.L., Chirn, G.W., Marr, T.G., Shapiro, B.A., Shasha, D., Zhang, K.: Combinatorial pattern discovery for scientific data: Some preliminary results. In: Proc. 1994 ACM SIGMOD Intl. Conf. on Management of Data. (1994) 115–125
Mannila, H., Toivonen, H.: Discovering generalized episodes using minimal occurrences. In: Knowledge Discovery and Data Mining. (1996) 146–151
Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1 (1997) 259–289
Weiss, G.M., Hirsh, H.: Learning to predict rare events in event sequences. In: Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining (KDD’98), New York, NY, AAAI Press, Menlo Park, CA (1998) 359–363
Yang, J., Wang, W., Yu, P.S.: Infominer: mining surprising periodic patterns. In: Proc. 7th ACM SIGKDD Conference. (2001) 395–400
Hatonen, K., Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H.: Knowledge discovery from telecommunication network alarm databases. In: Proc. 12th International Conference on Data Engineering. (1996) 115–122
Zaki, M.J., Lesh, N., Ogihara, M.: Planmine: Sequence mining for plan failures. In: Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining (KDD’98), New York, NY, ACM Press (1998) 369–373
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. 20th Int. Conf. Very Large Data Bases, Morgan Kaufmann (1994) 487–499
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. 2000 ACM SIGMOD Intl. Conf. on Management of Data, ACM Press (2000) 1–12
Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. In: Proc. 5th Int. Conf. Extending Database Technology. Volume 1057., Springer-Verlag (1996) 3–17
Zaki, M.J.: SPADE: An efficient algorithm for mining frequent sequences. Machine Learning 42 (2001) 31–60
Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., Hsu, M.: Freespan: Frequent pattern-projected sequential pattern mining. In: Proc. 6th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, ACM Press (2000) 355–359
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.C.: PrefixSpan mining sequential patterns efficiently by prefix projected pattern growth. In: Proc. 2001 Int. Conf. Data Engineering, Heidelberg, Germany (2001) 215–226
Srikant, R., Agrawal, R.: Mining generalized association rules. Future Generation Computer Systems 13 (1997) 161–180
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proc. 11th Int. Conf. on Data Engineering, Taipei, Taiwan, IEEE Computer Society Press (1995) 3–14
Savasere, A., Omiecinski, E., Navathe, S.B.: An efficient algorithm for mining association rules in large databases. In: The VLDB Journal. (1995) 432–444
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, X., Orlowska, M.E., Zhou, X. (2003). Finding Event-Oriented Patterns in Long Temporal Sequences. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8_3
Download citation
DOI: https://doi.org/10.1007/3-540-36175-8_3
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-04760-5
Online ISBN: 978-3-540-36175-6
eBook Packages: Springer Book Archive