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 -