Abstract: The paper considers two methods for processing sets of logical regularities of classes (LRC) found by training samples analysis. The first approach is based on the minimization of logical descriptions of classes. As a result of solving the problem of linear discrete optimization, the shortest logic description of each class is found. Each training object satisfies at least to one LRC of found irreducible subset of logical regularities. The second approach is based on the clustering of the set of LRC and selecting standards of derived clusters. The clustering problem is reduced to the clustering of representations of LRC set. Here each LRC is represented in the form of binary vector with different informative weight. A modification of the known method of "variance criterion minimization" for the case where the objects have different information weights is proposed. We present the results of illustrative experiments.

Keywords: classification, logical regularity of class, feature, clustering

ACM Classification Keywords: I.2.4 Artificial Intelligence Knowledge Representation Formalisms and Methods – Predicate logic; I.5.1 Pattern Recognition Models – Deterministic, H.2.8 Database Applications, Data mining

Link:

PROCESSING SETS OF CLASSES’ LOGICAL REGULARITIES

Anatoliy Gupal, Maxim Novikov, Vladimir Ryazanov

http://www.foibg.com/ijita/vol22/ijita22-01-p03.pdf