Rules in incomplete information systems
Information Sciences: an International Journal
Regularity analysis and its applications in data mining
Rough set methods and applications
Data mining in incomplete information systems from rough set perspective
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
A Comparison of Several Approaches to Missing Attribute Values in Data Mining
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Hierarchical Classifiers for Complex Spatio-temporal Concepts
Transactions on Rough Sets IX
Application of the Method of Editing and Condensing in the Process of Global Decision-making
Fundamenta Informaticae
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Granular Computing as a paradigm in Approximate Reasoning is concerned with granulation of available knowledge into granules that consists of entities similar in information content with respect to a chosen measure and with computing on such granules. Thus, operators acting on entities in a considered universe should factor through granular structures giving values similar to values of same operators in non---granular environment. Within rough set theory, proposed 25 years ago by Zdzisław Pawlak and developed thence by many authors, granulation is also a vital area of research. The first author developed a calculus with granules as well as a granulation technique based on similarity measures called rough inclusions along with a hypothesis that granules induced in data set universe of objects should lead to new objects representing them, and such granular counterparts should preserve information content in data. In this work, this hypothesis is tested with missing values in data and results confirm the hypothesis in this context.