Rough Computation Based on Similarity Matrix

Knowledge reduction is one of the most important tasks in rough set theory, and most types of reductions in this area are based on complete information systems. However, many information systems are not complete in real world. Though several extended relations have been presented under incomplete information systems, not all reduction approaches to these extended models have been examined. Based on similarity relation, the similarity matrix and the upper/lower approximation reduction are defined under incomplete information systems. To present similarity relation with similarity matrix, the rough computational methods based on similarity relation are studied. The heuristic algorithms for non-decision and decision incomplete information systems based on similarity matrix are proposed, and the time complexity of algorithms is analyzed. Finally, an example is given to illustrate the validity of these algorithms presented.

Authors and Affiliations

1. Department of Computer Science & Technology, Nanjing Audit University, Nanjing, 210029, China

   Huang Bing

2. Department of Automation, Nanjing University of Science & Technology, Nanjing, 210094, China

   Guo Ling & He Xin

3. School of Engineering Management, Nanjing University, Nanjing, 210093, China

   Xian-zhong Zhou

Editors and Affiliations

1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

   Lipo Wang

2. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

   Yaochu Jin

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© 2005 Springer-Verlag Berlin Heidelberg

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