Granular Concept Mapping and Applications

Abstract

This chapter presents a granular concept hierarchy (GCH) construction and mapping of the hierarchy for granular knowledge. A GCH is comprised of multilevel granular concepts with their hierarchy relations. A rough set based approach is proposed to induce the approximation of a domain concept hierarchy of an information system. A sequence of attribute subsets is selected to partition a granularity, hierarchically. In each level of granulation, reducts and core are applied to retain the specific concepts of a granule whereas common attributes are applied to exclude the common knowledge and generate a more general concept. A granule description language and granule measurements are proposed to enable mapping for an appropriate granular concept that represents sufficient knowledge so solve problem at hand. Applications of GCH are demonstrated through learning of higher order decision rules. © Springer-Verlag Berlin Heidelberg 2013.