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|Title:||A rough set based map granule|
|Keywords:||Concept hierarchy;Information granules;Map granules;Rough set theory|
|Citation:||International Conference on Rough Sets and Intelligent Systems Paradigms, RSEISP 2007|
|Abstract:||Data in an information system are usually represented and stored in a flat and unconnected structure as in a table. Underlying the data structure, there is a domain concept that is an understandable description for humans and supports other machine learning techniques. In this work, Map Granule (MG) construction is introduced. A MG comprises of multilevel granules with their hierarchy relations. We propose a rough set based granular computing to induce approximation of a domain concept hierarchy of an information system. An algorithm is proposed to select a sequence of attribute subsets which is necessary 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. The information granule relations are represented by a tree structure in which the relation strengths are defined by a rough ratio of specificness/coarseness.|
|Appears in Collections:||Mathematics: International Proceedings|
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