Minimal inhibitory association rules for almost all k -valued information systems

Pawel Delimata*, Mikhail Ju Moshkov, Andrzej Skowron, Zbigniew Suraj

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

There are three approaches to use inhibitory rules in classifiers: (i) lazy algorithms based on an information about the set of all inhibitory rules, (ii) standard classifiers based on a subset of inhibitory rules constructed by a heuristic, and (iii) standard classifiers based on the set of all minimal (irreducible) inhibitory rules. The aim of this chapter is to show that the last approach is not feasible (from computational complexity point of view). We restrict our considerations to the class of k-valued information systems, i.e., information systems with attributes having values from {0,..., k-1}, where k >2. Note that the case k=2 was considered earlier in [51].

Original languageEnglish (US)
Title of host publicationInhibitory Rules in Data Analysis
Subtitle of host publicationA Rough Set Approach
Pages31-41
Number of pages11
Volume163
DOIs
StatePublished - 2009
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume163
ISSN (Print)1860-949X

ASJC Scopus subject areas

  • Artificial Intelligence

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