The cluster analysis and the fonnal concept analysis are both used to identity significiant groups of similar objects. Rice & Siff’s algorithm for the clustering joins these two methods in the case where the values of an object-attribute model are 1 or 0 and often reduce an amount of concepts. We use a certain type of fuzzification of a concept lattice for generalization of this clustering algorithm in the fuzzy case. For the purpose of finding dependencies between the objects in the clusters we use our method of the induction of generalized annotated programs based on multiple using of the crisp inductive logic programming. Since our model contains fuzzy data, it should have work with a fuzzy background knowledge and a fuzzy set of examples - which are not divided clearly into positive and negative classes, but there is a monotone hierarchy (degree, preference) of more or less positive / negative examples. We have made experiments on data describing business competitiveness of Slovak companies.
The frequent patterns discovery is one of the most important data
mining tasks. We introduce RAP, the hrst systém for finding first-order maximal frequent patterns. We describe search strategies and methods of pruning the search space. RAP which generates long patterns much faster than other systems has been ušed for feature construction for propositional cis well as multi-relational data. We prove that a partial search for maximal frequent patterns as new features is competitive with other approaches and results in classification accuracy increase.