Table representations of granulations revisited

Authors:
I-Jen Chiang;Tsau Young Lin;Yong Liu
Affiliations:
Graduate Institute of Medical Informatics, Taipei Medical University, Taipei, Taiwan;Department of Computer Science, San Jose State University, San Jose, California;College of Computer Science, Zhejiang University, Hangzhou, China
Venue:
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Year:
2005

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Abstract

This paper examines the knowledge representation theory of granulations. The key strengths of rough set theory are its capabilities in representing and processing knowledge in table format. For general granulation such capabilities are unknown. For single level granulation, two initial theories have been proposed previously by one of the authors. In this paper, the theories are re-visited, a new and deeper analysis is presented: Granular information table is an incomplete representation, so computing with words is the main method of knowledge processing. However for symmetrical granulation, the pre-topological information table is a complete representation, so the knowledge processing can be formal.