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应用科学学报 ›› 2009, Vol. 27 ›› Issue (6): 644-650.

• 控制与系统 • 上一篇    下一篇

基于满意度控制的机场应急救援决策规则的数据挖掘

姜静逸1， 韩松臣1， 汤新民1， 倪金霞1， 崔国山2   

  1. 1. 南京航空航天大学民航学院，南京210016
    2. 中华人民共和国南通海事局，江苏南通226005
  • 收稿日期:2009-07-01 修回日期:2009-10-14 出版日期:2009-11-25 发布日期:2009-11-30
  • 通信作者: 韩松臣，教授，博导，研究方向：交通运输规划与管理，E-mail: hansongchen001@sina.com
  • 基金资助:

    国家自然科学基金(No.60776813)资助项目

Data Mining for Airport Emergency Rescue Decision-Making Rule Controlled by Satisfaction Degree

JIANG Jing-yi1, HAN Song-chen1, TANG Xin-min1, NI Jin-xia1, CUI Guo-shan2   

  1. 1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
    2. Nantong Maritime Safety Administration, People’s Republic of China，Nantong 226005, Jiangsu Province, China
  • Received:2009-07-01 Revised:2009-10-14 Online:2009-11-25 Published:2009-11-30

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1420

被引次数

4

摘要：

摘要: 机场应急救援工作的规模决策对于抢救生命财产至关重要. 针对目前救援规模决策缺乏合理规则指导的问题，
提出了一种基于满意度控制的数据挖掘方法. 该方法将满意度理论中的选择函数和拒绝函数植入数据挖掘关联规则算法
中，在保持样本完好性的同时识别异样数据，控制挖掘进程，建立有效的机场应急救援辅助决策规则库，用以辅助决策
者作出更为科学合理的决策. 实验结果表明，该算法能挖掘出合理的应急救援规模决策规则，提高挖掘的准确性和效率.

关键词: 机场应急救援, 规模决策, 满意度, 关联规则, 数据挖掘

Abstract:

The scale of decision-making in airport emergency rescue operations is extremely important to save
people and properties. In view of the current absence of reasonable rules for the rescue scale decision-making, a data
mining method based on the satisfaction control is proposed. The selection and rejection functions of the satisfaction
theory are planted into the association rule algorithm in the data mining. It tries to identify abnormal data while
keeps integrity of the samples, and controls the mining process. An effective rule base of the airport emergency
rescue is established to assist decision-making, which can help the decision maker make a more reasonable decision.
Experiment result shows that the proposed method provides reasonable rules for the emergency rescue scale decisionmaking,
and improves accuracy and efficiency of the data mining.

Key words: airport emergency rescue, scale decision-making, satisfaction degree, association rule, data mining

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