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2012 (Vol. 4, Issue: 02)
Article Information:

Rules Extraction by Clustering Artificial Fish-swarm and Rough Set

Yingwei Huang, Bo Fu, Xinchen Cai, Xin Xing, Xinxing Yuan and Lu Yu
Corresponding Author:  Yingwei Huang 

Key words:  Artificial fish-swarm, clustering, discretization, rough set, rule extraction, ,
Vol. 4 , (02): 127-130
Submitted Accepted Published
2011 September, 23 2011 November, 02 2012 January, 15

Due to the ill-conditioned problem caused by inefficient discretization approaches, it is difficult for the traditional rough set theory to extract accurate rules. And the continuous value needs to be discretized in the process of rule extraction. Then in this paper, a method based on clustering Artificial Fish-Swarm Algorithm (AFSA) and rough set theory is proposed to extract decision rules. Firstly, the clustering algorithm is used to classify attribute values in accordance with decision attributes. Secondly, the artificial fish-swarm algorithm is used to discretize the continuous attributes and to reduce the decision table. The experimental results indicate that the decision rules derived from the proposed method are much simpler and more precise.
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  Cite this Reference:
Yingwei Huang, Bo Fu, Xinchen Cai, Xin Xing, Xinxing Yuan and Lu Yu, 2012. Rules Extraction by Clustering Artificial Fish-swarm and Rough Set.  Research Journal of Applied Sciences, Engineering and Technology, 4(02): 127-130.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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