Аннотация:The paper considers two methods for processing se
ts of logical regularities of classes (LRC)
found by training samples analysis. The first appr
oach is based on the mi
nimization 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 traini
ng object satisfies at l east to one LRC of found
irreducible subset of logical regul arities. The second approach is bas
ed 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 repres
ented in the form of binary vector with different
informative weight. A modification of
the known method of "variance crit
erion minimization" for the case
where the objects have different information weights
is proposed. We present t
he results of illustrative
experiments.