Interpretation of extended Pawlak flow graphs using granular computing

  • Authors:

  • Huawen Liu;Jigui Sun;Huijie Zhang

  • Affiliations:

  • College of Computer Science, Jilin University, Changchun, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun, China;College of Computer Science, Jilin University, Changchun, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun, China;College of Computer Science, Jilin University, Changchun, China and College of Computer, Northeast Normal University, Changchun, China

  • Venue:
  • Transactions on rough sets VIII
  • Year:
  • 2008

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Abstract

In this paper, we mainly discuss the relationship between the extended Pawlak flow graph (EFG) with granular computing (GrC), and develop a both simple and concrete model for EFG using GrC. The distinct advantage is that we can resort to merits of GrC to benefit us in analyzing and processing data using flow graph, for its structure is inherently consistent with GrC, which provides us with both structured thinking at the philosophical level and structured problem solving at the practical level. In pursuit of our purpose, at first, EFG will be mainly discussed in three aspects under GrC, namely, granulation of EFG, some relationships and operations of granules. Under the framework of GrC model, inference and reformation in EFG can be easily implemented in virtue of decomposition and composition of granules, respectively. Based on this scheme, two efficient reduction algorithms about EFG are also proposed.