Abstract
In this monograph, we studied inhibitory decision and association rules. We showed that using inhibitory rules one can describe more knowledge encoded in information and decision systems than in the case of deterministic (standard) rules.
Unfortunately, for almost all k-valued information systems with the polynomial number of objects in the number of attributes the number of minimal (irreducible) inhibitory association rules is not polynomial in the number of attributes. In some sense analogous situation is with minimal inhibitory decision rules.
In such a situation, we can either use some heuristics for generating of relatively small sets of “important” inhibitory rules, or use lazy classification algorithms which in polynomial time can find an information about the whole set of true and realizable inhibitory rules for a given information or decision system.
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 subscriptionsAuthor information
Authors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Delimata, P., Moshkov, M.J., Skowron, A., Suraj, Z. (2009). Final Remarks. In: Inhibitory Rules in Data Analysis. Studies in Computational Intelligence, vol 163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85638-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-85638-2_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85637-5
Online ISBN: 978-3-540-85638-2
eBook Packages: EngineeringEngineering (R0)