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Vol. 167, Issue 3, March 2014, pp. 43-49

 

Bullet

 

Mining Association Rules from Airport Noise Value Among Multiple Monitoring Points
 

1 Fei Gu, 1, 2, 3 Tao Xu, 2, 3 Zonglei Lv

1 College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2 College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China
3 Information Technology Research Base of Civil Aviation Administration of China, Tianjin 300300, China
1 Tel: +8613290350319

1 E-mail: jintiangufei@163.com

 

Received: 20 December 2013 /Accepted: 28 February 2014 /Published: 31 March 2014

Digital Sensors and Sensor Sysstems

 

Abstract: There are a lot of links among monitoring points of airport noise. To mine association rules among these monitoring points is very important in order to predict airport noise scientifically and effectively. Due to the low efficiency of the Apriori algorithm for mining association rules, this paper proposes a new algorithm called 'Adapt to Noise Set of Airport-Apriori (ATNSOA- Apriori)'. According to the characteristics of monitoring data sets of airport noise, this algorithm optimizes the monitoring data to improve the validity of the monitoring data sets and uses arrays to store items to lower the number of traversing database. As a result, the efficiency of mining association rules is improved. Taking the actual noise monitoring data in a domestic airport in China for example, the experimental results show that the ATNSOA - Apriori algorithm can deal with monitoring data sets of airport noise more effectively and mine the useful association rules more quickly. The proposed algorithm, therefore, is of vital significance for predicting the value of monitoring points and evaluating the effectiveness of the value of monitoring points.

 

Keywords: Airport noise, Association rules, Monitoring points, Apriori algorithm, ATNSOA-Apriori algorithm.

 

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