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.
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Sonamthiang, S., Cercone, N., Naruedomkul, K. (2007). A Rough Set Based Map Granule. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2007. Lecture Notes in Computer Science(), vol 4585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73451-2_31
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DOI: https://doi.org/10.1007/978-3-540-73451-2_31
Publisher Name: Springer, Berlin, Heidelberg
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