TY - GEN

T1 - Minimal inhibitory association rules for almost all k -valued information systems

AU - Delimata, Pawel

AU - Moshkov, Mikhail Ju

AU - Skowron, Andrzej

AU - Suraj, Zbigniew

PY - 2009

Y1 - 2009

N2 - 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].

AB - 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].

UR - http://www.scopus.com/inward/record.url?scp=51849164490&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-85638-2_3

DO - 10.1007/978-3-540-85638-2_3

M3 - Conference contribution

AN - SCOPUS:51849164490

SN - 9783540856375

VL - 163

T3 - Studies in Computational Intelligence

SP - 31

EP - 41

BT - Inhibitory Rules in Data Analysis

ER -