A rough set based map granule
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)...
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th-mahidol.243872018-08-24T08:56:36Z A rough set based map granule Sumalee Sonamthiang Nick Cercone Kanlaya Naruedomkul Mahidol University York University Computer Science Mathematics 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. 2018-08-24T01:48:03Z 2018-08-24T01:48:03Z 2007-12-01 Conference Paper Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol.4585 LNAI, (2007), 290-299 16113349 03029743 2-s2.0-38049075792 https://repository.li.mahidol.ac.th/handle/123456789/24387 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=38049075792&origin=inward |
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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. |
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Mahidol University |
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Mahidol University Sumalee Sonamthiang Nick Cercone Kanlaya Naruedomkul |
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Conference or Workshop Item |
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Sumalee Sonamthiang Nick Cercone Kanlaya Naruedomkul |
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Sumalee Sonamthiang |
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A rough set based map granule |
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A rough set based map granule |
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A rough set based map granule |
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A rough set based map granule |
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A rough set based map granule |
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rough set based map granule |
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2018 |
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https://repository.li.mahidol.ac.th/handle/123456789/24387 |
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1763491604217200640 |