Abstract
This paper continues the study of mining patterns from the real world data. Association rules that respects the semantics modeled by binary relations are called binary semantic association rules. By experiments we find that semantic computation is necessary, efficient and fruitful. It is necessary, because we find the supports of length 2 candidate is quite high in randomly generated data. It is efficient, because the checking of semantics constraints occurs only at length 2. It is fruitful the additional cost is well compensated by the saving in pruning away (non-semantic) association rules.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agrawal, R., R. Srikant, “Fast Algorithms for Mining Association Rules,” in Proceeding of 20th VLDB Conference San Tiago, Chile, 1994.
W. Chu and Q. Chen, “Neighborhood and associative query answering,” Journal of Intelligent Information Systems, vol 1, 355–382, 1992.
K. Engesser, Some connections between topological and Modal Logic, Mathematical Logic Quarterly, 41, 49–64, 1995.
T. Y. Lin, “Data Mining and Machine Oriented Modeling: A Granular Computing Approach,” Journal of Applied Intelligence, Kluwer, Vol. 13, No 2, September/October, 2000, pp.113–124.
T. Y. Lin, “Data Mining: Granular Computing Approach.” In: Methodologies for Knowledge Discovery and Data Mining, Lecture Notes in Artificial Intelligence 1574, Third Pacific-Asia Conference, Beijing, April 26–28, 1999, 24–33.
T. Y. Lin, “Granular Computing: Fuzzy Logic and Rough Sets.” In: Computing with words in information/intelligent systems, L.A. Zadeh and J. Kacprzyk (eds), Springer-Verlag, 183–200, 1999
T. Y. Lin, “Granular Computing on Binary Relations I: Data Mining and Neighborhood Systems.” In: Rough Sets In Knowledge Discovery, A. Skoworn and L. Polkowski (eds), Springer-Verlag, 1998, 107–121.
T. Y. Lin, “Granular Computing on Binary Relations II: Rough Set Representations and Belief Functions.” In: Rough Sets In Knowledge Discovery, A. Skoworn and L. Polkowski (eds), Springer-Verlag, 1998, 121–140.
T. Y. Lin, “Rough Set Theory in Very Large Databases,” Symposium on Modeling, Analysis and Simulation, CESA’96 IMACS Multi Conference (Computational Engineering in Systems Applications), Lille, France, July 9–12, 1996, Vol. 2 of 2, 936–941.
T. Y. Lin, “Neighborhood Systems and Approximation in Database and Knowledge Base Systems,” Proceedings of the Fourth International Symposium on Methodologies of Intelligent Systems , Poster Session, October 12 – 15, pp. 7586, 1989.
T. Y. Lin, “Topological Data Models and Approximate Retrieval and Reasoning,” in: Proceedings of 1989 ACM Seventeenth Annual Computer Science Conference, February 21–23, Louisville, Kentucky, 1989, 453.
T. Y. Lin, “Neighborhood Systems and Relational Database”. Abstract, Proceedings of CSC ’88, February, 1988, pp. 725.
Eric Louie and T.Y. Lin, “Finding Association Rules using Fast tit Uomputation: Machine-Oriented Modeling.” In: Proceeding of 12th International Symposium ISMIS2000, Charlotte, North Carolina, Oct 11–14, 2000. Lecture Notes in AI 1932. 486–494.
T. Y. Lin and E. Louie, “A Data Mining Approach using Machine Oriented Modeling: Finding Association Rules using Canonical Names.”. In: Proceeding of 14th Annual International Symposium Aerospace/Defense Sensing, Simulation, and Controls , SPIE Vol 4057, Orlando, April 24–28, 2000, pp.148–154
T. Y. Lin, and Y.Y. Yao “Mining Soft Rules Using Rough Sets and Neighborhoods.” In: Symposium on Modeling, Analysis and Simulation, IMACS Multiconference (Computational Engineering in Systems Applications), Lille, France, July 9–12, 1996, Vol. 2 of 2, 1095–1100.
Z. Pawlak, Rough sets. Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, 1991
W. Sierpenski and C. Krieger, General Topology, University of Toronto Press 1952.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Lin, T.Y., Louie, E. (2003). Association Rules with Additional Semantics Modeled by Binary Relations. In: Inuiguchi, M., Hirano, S., Tsumoto, S. (eds) Rough Set Theory and Granular Computing. Studies in Fuzziness and Soft Computing, vol 125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36473-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-36473-3_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05614-7
Online ISBN: 978-3-540-36473-3
eBook Packages: Springer Book Archive