Abstract
Sequence mining is an active research field of data mining because algorithms designed in that domain lead to various valuable applications. To increase efficiency of basic sequence mining algorithms, generally based on a levelwise approach, more recent algorithms try to introduce some constraints to prune the search space during the discovery process. Nevertheless, existing algorithms are actually limited to extract frequent sequences made up of items of a database. In this paper, we generalize the notion of sequence to define what we call logical sequence where each element of a sequence may contain some logical variables. Then we show how we can extend constrained sequence mining to constrained frequent logical sequence mining1.
Research partially funded by the European contract cInQ IST 2000-26469-FET.
Paper presented with the financial help of ILPnet2.
This paper is an extended english version of [11].
This is a preview of subscription content, log in via an institution to check access.
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
R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. of the 20th Intl. Conf. on Very Large Databases, pages 487–499, Santiago de Chile, Chile, September 1994.
R. Agrawal and R. Srikant. Mining sequential patterns. In Proc. of the 11th Intl. Conf. Data Engineering, pages 3–14, Taipei, Taiwan, March 1995.
H. Blockeel and L. De Raedt. Top-down induction of first-order logical decision trees. Artificial Intelligence, 101(1–2):285–297, June 1998.
L. Dehaspe and H. Toivonen. Discovery of frequent Datalog patterns. Data Mining and Knowledge Discovery, 3(1):7–36, 1999.
S. Dzeroski and N. Lavrac, editors. Relational Data Mining. Springer-Verlag, 2001.
M. N. Garofalakis, R. Rastogi, and K. Shim. Spirit: Sequential pattern mining with regular expression constraints. Technical report, Bell Laboratories, 1999.
M. N. Garofalakis, R. Rastogi, and K. Shim. Spirit: Sequential pattern mining with regular expression constraints. In VLDB’99, Proc. of 25th Intl. Conf. on Very Large Databases, pages 223–234, Edinburgh, Scotland, UK, September 1999.
N. Jacobs and H. Blockeel. From shell logs to shell scripts. In Proc. of the 11th Intl. Conf. on Inductive Logic Programming, pages 80–90, Strasbourg, France, September 2001.
S. D. Lee and L. De Raedt. Constraint based mining of first-order sequences in seqlog. In Proc. of the WorkShop on Multi-Relational Data Mining, colocated with ACM SIGKDD’02, Edmonton, Alberta, Canada, July 2002.
H. Mannila, H. Toivonen, and A. I. Verkamo. Discovering frequent episodes in sequences. In Proc. of the 1st Intl. Conf. on Knowledge Discovery and Data Mining, pages 210–215, Montreal, Canada, August 1995.
C. Masson and F. Jacquenet. D couverte de s quences logiques fr quentes sous contraintes. In Actes du 13ème Congrès Francophone de Reconnaissance des Formes et Intelligence Artificielle, pages 673–684, Angers, France, January 2002. In French.
R. T. Ng, V. S. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained associations rules. In Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data, pages 13–24, Seattle, Washington, USA, June 1998.
R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations and performance improvements. In Proc. of the 5th Intl. Conf. Extending Database Technology, pages 3–17, Avignon, France, March 1996.
M. J. Zaki. Sequence mining in categorical domains: Incorporating constraints. In Proc. of the 9th Conf. on Information and Knowledge Management, pages 422–429, McLean, VA, November 2000.
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
Masson, C., Jacquenet, F. (2003). Mining Frequent Logical Sequences with SPIRIT-LoG. In: Matwin, S., Sammut, C. (eds) Inductive Logic Programming. ILP 2002. Lecture Notes in Computer Science(), vol 2583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36468-4_11
Download citation
DOI: https://doi.org/10.1007/3-540-36468-4_11
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00567-4
Online ISBN: 978-3-540-36468-9
eBook Packages: Springer Book Archive