Inference and Reformation in Flow Graphs Using Granular Computing

Authors:
Huawen Liu;Jigui Sun;Changsong Qi;Xi Bai
Affiliations:
College of Computer Science, Jilin University, Changchun 130012, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun 130012, China;College of Computer Science, Jilin University, Changchun 130012, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun 130012, China;College of Computer Science, Jilin University, Changchun 130012, China and Department of Computer, Tonghua Normal College, Tonghua 134002, China;College of Computer Science, Jilin University, Changchun 130012, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun 130012, China
Venue:
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Year:
2007

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Abstract

Flow graph (FG) is a new mathematical model which can be used for representing, analyzing, and discovering knowledge in databases. Due to its well-structured characteristics of network, FG is naturally consistent with granular computing (GrC). Meanwhile, GrC provides us with both structured thinking at the philosophical level and structured problem solving at the practical level. In this paper, the relationship between FG and GrC will be discussed from three aspects under GrC at first, and then inference and reformation in FG can be easily implemented in virtue of decomposition and composition of granules, respectively. As a result of inference and reformation, the reformed FG is a reduction of the original one.