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We present a hierarchical scheme for synthesis of concept approximations based on given data and domain knowledge. We also propose a solution, founded on rough set theory, to the problem of constructing the approximation of higher level concepts by composing the approximation of lower level concepts. We examine the effectiveness of the layered learning… (More)

Many learning methods ignore domain knowledge in synthesis of concept approximation. We propose to use hierarchical schemes for learning approximations of complex concepts from experimental data using inference diagrams based on domain knowledge. Our solution is based on the rough set and rough mereological approaches. The effectiveness of the proposed… (More)

We consider a synthesis of complex objects by multi-agent system based on rough mereological approach. Any agent can produce complex objects from parts obtained from his sub-agents using some composition rules. Agents are equipped with decision tables describing (partial) speciications of their synthesis tasks. We investigate some problems of searching for… (More)

Searching for patterns is one of the main goals in data mining. Patterns have important applications in many KDD domains like rule extraction or classiication. In this paper we present some methods of rule extraction by generalizing the existing approaches for the pattern problem. These methods, called partition of attribute values or grouping of attribute… (More)

We present an efficient method for decision tree construction from large data sets, which are assumed to be stored in database servers, and be accessible by SQL queries. The proposed method minimizes the number of simple queries necessary to search for the best splits (cut points) by employing " divide and conquer " search strategy. To make it possible, we… (More)

ASSOCIATION RULE (see 1]) extraction methods have been developed as the main methods for mining of real life data, in particular in Basket Data Analysis. In this paper we present a n o vel approach to generation of association rule, based on Rough Set and Boolean reasoning methods. We s h o w the relationship between the problems of association rule… (More)

We consider several basic classes of tolerance relations among objects. These (global) relations are deened from some predeened similarity measures on values of attributes. A tolerance relation in a given class of tolerance relations is optimal with respect to a given decision table A if it contains only pairs of objects with the same decision and the… (More)

We consider decision tables with real value conditional attributes and we present a method for extraction of features deened by hyperplanes in a multi-dimensional aane space. These new features are often more relevant for object classiication than the features deened by hyperplanes parallel to axes. The method generalizes an approach presented in 18] in… (More)