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Title: A rough set based map granule
Authors: Kanlaya Naruedomkul
Keywords: Concept hierarchy;Information granules;Map granules;Rough set theory
Issue Date: 2007
Publisher: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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.
Description: Scopus
ISSN: 03029743
Appears in Collections:Mathematics: International Proceedings

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