Abstract
This paper refers to the notion of minimal pattern in relational databases. We study the analogy between two concepts: a local reduct, from the rough set theory, and a jumping emerging pattern, originally defined for transactional data. Their equivalence within a positive region and similarities between eager and lazy classification methods based on both ideas are demonstrated. Since pattern discovery approaches vary significantly, efficiency tests have been performed in order to decide, which solution provides a better tool for the analysis of real relational datasets.
The research has been partially supported by grant No 3 T11C 002 29 received from Polish Ministry of Education and Science.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Fan, H.: Efficient Mining of Interesting Emerging Patterns and Their Effective Use in Classification. University of Melbourne: PhD thesis (2004)
Dong, G., Li, J.: Mining border descriptions of emerging patterns from dataset pairs. Knowl. Inf. Syst. 8(2), 178–202 (2005)
Wroblewski, J.: The Adaptive Methods for Object Classification. Warsaw University, Institute of Informatics: PhD thesis (2002)
Terlecki, P., Walczak, K.: On the relation between rough set reducts and jumping emerging patterns. Information Sciences (2006) (to be published)
Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowinski, R. (ed.) Intelligent Decision Support, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)
Bazan, J., Nguyen, H.S., Nguyen, S.H., Synak, P., Wroblewski, J.: Rough set algorithms in classification problem. Rough set methods and applications: new developments in knowl. disc. in inf. syst., 49–88 (2000)
Li, J., Dong, G., Ramamohanarao, K., Wong, L.: Deeps: A new instance-based lazy discovery and classification system. Mach. Learn. 54(2), 99–124 (2004)
Shan, N., Ziarko, W.: An incremental learning algorithm for constructing decision rules. In: Ziarko, W. (ed.) Rough Sets, Fuzzy Sets and Knowledge Discovery, pp. 326–334. Springer, Berlin (1994)
Terlecki, P., Walczak, K.: Attribute set dependence in apriori-like reduct computation. In: Rough Sets and Knowl. Techn. (2006) (to be published)
Jürgens, M., Lenz, H.-J.: Tree based indexes vs. bitmap indexes - a performance study. Int. Journal of Cooperative Inf. Syst. 10(3), 355–376 (2001)
Newman, C.B.D.J., Hettich, S., Merz, C.: UCI repository of machine learning databases (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Terlecki, P., Walczak, K. (2006). Local Reducts and Jumping Emerging Patterns in Relational Databases. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_38
Download citation
DOI: https://doi.org/10.1007/11908029_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47693-1
Online ISBN: 978-3-540-49842-1
eBook Packages: Computer ScienceComputer Science (R0)