Publication: A rough set based map granule
Issued Date
2007-12-01
Resource Type
ISSN
16113349
03029743
03029743
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2-s2.0-38049075792
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Mahidol University
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SCOPUS
Bibliographic Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol.4585 LNAI, (2007), 290-299
Suggested Citation
Sumalee Sonamthiang, Nick Cercone, Kanlaya Naruedomkul A rough set based map granule. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol.4585 LNAI, (2007), 290-299. Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/24387
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Title
A rough set based map granule
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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. © Springer-Verlag Berlin Heidelberg 2007.