Abstract
In data mining, searching for frequent patterns is a common basic operation. It forms the basis of many interesting decision support processes. In this paper we present a new type of patterns, binary expressions. Based on the properties of a specified binary test, such as reflexivity, transitivity and symmetry, we construct a generic algorithm that mines all frequent binary expressions.
We present three applications of this new type of expressions: mining for rules, for horizontal decompositions, and in intensional database relations.
Since the number of binary expressions can become exponentially large, we use data mining techniques to avoid exponential execution times. We present results of the algorithm that show an exponential gain in time due to a well chosen pruning technique.
Research Assistant of the Fund for Scientific Research - Flanders (Belgium)(F.W.O. - Vlaanderen).
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
R. Agrawal, T. Imilienski, and A. Swami. Mining association rules between sets of items in large databases. In Proc. ACM SIGMOD, Washington, D.C., 1993
T. Calders, and J. Paredaens. Mining Binary Expressions: Applications and Algorithms. Technical Report, Universiteit Antwerpen, Belgium, June 2000.
P. De Bra. Horizontal decompositions based on functional-dependency-set-implications. In ICDT. Springer-Verlag, 1986.
L. Dehaspe. Frequent pattern discovery in first-order logic. PhD thesis, Katholieke Universiteit Leuven, Belgium, Dec. 1998.
J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In Proc. ACM SIGMOD, 2000
H. Mannila and H. Toivonen. Levelwise search and borders of theories in knowledge discovery. In Data Mining and Knowledge Discovery 1(3), 1997.
J. Pei, J. Han, B. Mortazavi-Asl, and H. Zhu. Mining access patterns efficiently from web logs. In PAKDD, 2000.
M. Y. Vardi. The decision problem for database dependencies. In Inf. Proc. Letters 12(5), 1981.
J. Wijsen, R. Ng, and T. Calders. Discovering roll-up dependencies. In Proc. ACM SIGKDD, 1999.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Calders, T., Paredaens, J. (2000). Mining Frequent Binary Expressions. In: Kambayashi, Y., Mohania, M., Tjoa, A.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2000. Lecture Notes in Computer Science, vol 1874. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44466-1_40
Download citation
DOI: https://doi.org/10.1007/3-540-44466-1_40
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67980-6
Online ISBN: 978-3-540-44466-4
eBook Packages: Springer Book Archive