In decision support system for spatial site selection, the fuzzy synthetic evaluation is a useful way. However, the method can’t pay attention to the randomness in factors. To remedy the problem, this paper proposes ...In decision support system for spatial site selection, the fuzzy synthetic evaluation is a useful way. However, the method can’t pay attention to the randomness in factors. To remedy the problem, this paper proposes a clouded-base fuzzy approach which combines advantages of cloud transform and fuzzy synthetic evaluation. The cloud transform considers the randomness in the factors and product the higher concept layer for data mining. At the same time, the check mechanism controls the quality of partitions in factors. Then the fuzzy approach was used to get final evaluation value with randomness and fuzziness. It make the final result is optimization. Finally, performance evaluations show that this approach spent less runtime and got more accuracy than the fuzzy synthetic. The experiments prove that the proposed method is faster and more accuracy than the original method.展开更多
基金This research is supported by the MIC ( Ministry of Information and Communication) , Korea ,underthe ITRC(Information Technology Research Center) support program supervised by the IITA(Institute of Information Tech-nology Assessment)
文摘In decision support system for spatial site selection, the fuzzy synthetic evaluation is a useful way. However, the method can’t pay attention to the randomness in factors. To remedy the problem, this paper proposes a clouded-base fuzzy approach which combines advantages of cloud transform and fuzzy synthetic evaluation. The cloud transform considers the randomness in the factors and product the higher concept layer for data mining. At the same time, the check mechanism controls the quality of partitions in factors. Then the fuzzy approach was used to get final evaluation value with randomness and fuzziness. It make the final result is optimization. Finally, performance evaluations show that this approach spent less runtime and got more accuracy than the fuzzy synthetic. The experiments prove that the proposed method is faster and more accuracy than the original method.
