Regularity analysis and its applications in data mining
Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
Analogy-based reasoning in classifier construction
Transactions on Rough Sets IV
On Classifying Mappings Induced by Granular Structures
Transactions on Rough Sets IX
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The paradigm of Granular Computing has quite recently emerged as an area of research on its own; in particular, it is pursued within rough set theory initiated by Zdzisław Pawlak. Granules of knowledge consist of entities with a similar in a sense information content. An idea of a granular counterpart to a decision/information system has been put forth, along with its consequence in the form of the hypothesis that various operators, aimed at dealing with information, should factorize sufficiently faithfully through granular structures [7], [8]. Most important such operators are algorithms for inducing classifiers. We show results of testing few well-known algorithms for classifier induction on well---used data sets from Irvine Repository in order to verify the hypothesis. The results confirm the hypothesis in case of selected representative algorithms and open a new prospective area of research.