Table representations of granulations revisited pre-topological information tables

I-Jen Chiang, Tsau Young Lin, Yong Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationRough Sets, Fuzzy Sets, Data Mining, and Granular Computing - 10th International Conference, RSFDGrC 2005, Proceedings
PublisherSpringer Verlag
Pages728-737
Number of pages10
ISBN (Print)3540286535, 9783540286530
DOIs
Publication statusPublished - 2005
Event10th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005 - Regina, Canada
Duration: Aug 31 2005Sept 3 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3641 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005
Country/TerritoryCanada
CityRegina
Period8/31/059/3/05

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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