Design and Implementation of Airline Customer Segmentation System Based on Ant Colony Clustering Algorithm

Abstract:

Segmentation based on customer value and needs can better guide marketing decision-making of airlines as well as better understand needs of high-value passengers. To address customer segmentation in Customer Relationship Management (CRM), the paper proposed and designed airline customer segmentation system structure based on ant colony clustering. The key ant colony clustering algorithm was also designed and implemented. The ant colony clustering algorithm mainly used adaptively adjusted group similarity to perform clustering and access to initial clustering result. Then all data representation points and abnormal data were inputted into lattice plane scattered randomly. Ant colony algorithm was used for clustering once again and corresponding class label was used to delete abnormal values and obtain complete clusters. Data test example based on ant colony clustering customer analysis platform illustrated its feasibility and effectiveness

• Periodical: Advanced Materials Research (Volumes 433-440)
• Pages: 3357-3361
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.433-440.3357
• Online since: January 2012
• Authors: Li Feng Wei
• Keywords: Airlines, Ant Colony Clustering, Customer Segmentation

References

[1] Zhan-xin Qin and Lei Wang: Development Trend of Civil Aviation and Strategic Choice of China Civil Aviation, Management World. vol. 3, 2004, pp.137-138.

[2] Bin Shen and Qing-sheng Wu: Analysis of Enhancing the Competitiveness of China's Airlines, Journal of Dalian University of Technology (Social Sciences). vol. 24, 2003, pp.33-37.

[3] Shao-cheng Liu: Competence Research on Chinese Civil Aviation Enterprises, Chinese Civil Aviation. vol. 55, 2005, pp.22-24.

[4] Chidanand A. and Sholom W: Data Mining with Decision Trees and Decision Rules, Future Generation Computer Systems. vol. 13, 1997, pp.197-210.

DOI: 10.1016/s0167-739x(97)00021-6

[5] Wei-jiao Zhang, Chun-huang Liu and Xiao-feng Yin: Application Research on Data Mining Using Ant Colony Algorithm, Computer Engineering and Applications. vol. 28, 2004, pp.171-173.

[6] An-rong Xue, Lin Yao, Shi-guang Ju, Wei-he Chen and Han-da Ma: Survey of Outlier Mining, Computer Science. vol. 35, 2008, pp.13-18.

[7] Ze-yu Sun and Wei Wei: A Study on Improvement of Ant Colony Algorithm Strategies Combinational Optimization, Computer Simulations. vol. 27, 2010, pp.194-197.