Integration of artificial immune system and k-means algorithm for customer clustering

Applied Artificial Intelligence

Volumn 28, Issue 6, 2014, Pages 577-596

  Kuo, R J ^a   Chiang, N J ^b   Chen, Z Y ^c  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b Micro Star Int'l Co Ltd   (Taiwan)

^c DE LIN INSTITUTE OF TECHNOLOGY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

IMMUNE SYSTEM; PARTICLE SWARM OPTIMIZATION (PSO);

ARTIFICIAL IMMUNE SYSTEM; BASED CLUSTERING; CLUSTERING PROBLEMS; CLUSTERINGS; COMPUTATIONAL RESULTS; GLASS WINE; K-MEAN ALGORITHMS; K-MEANS; PARTICLE SWARM; SWARM OPTIMIZATION;

CLUSTERING ALGORITHMS;

EID: 84904468646     PISSN: 08839514     EISSN: 10876545     Source Type: Journal    
DOI: 10.1080/08839514.2014.923167     Document Type: Article

References (55)

1. Al-Sultan, K. S. 1995. A Tabu s earch approach to the clustering problem. Pattern Recognition 28(9):1443-1451. (Pubitemid 26442212)
2. Amraee, T., A. M. Ranjbar, B. Mozafari, and N. Sadati. 2007. An enhanced under-voltage load-shedding scheme to provide voltage stability. Electric Power Systems Research 77(8):1038-1046. (Pubitemid 46505369)
3. Bauer, C. L. 1988. A direct mail customer purchase model. Journal of Direct Marketing 2(3):16-24.
4. Beze rra, G., T. Barra, L. de Castro, and F. Von Zuben. 2005. Adaptive radius immune algorithm for data clustering. In Proceedings of the 4th international conference on artificial immune systems 3627:290 -303, ed. C. Jacob, M. Pilat, et al. Berlin, Heidelberg: Springer-Verlag. (Pubitemid 41472730)
5. Cao, Y., and Y. Li. 2007. An intelligent fuzzy-based recommendation system for consumer electronic products. Expert Systems with Applications 33(1):230-240. (Pubitemid 44959933)
6. Carpenter, G. A., and S. Grossberg. 1987. ART 2: Self-organization of stable category recognition codes for analog input patterns. Applied Optics 26(23):4919-1930.
7. Chiu, C .-Y., I. T. Kuo, and C.-H. Lin. 2009. Applying artificial immune system and ant algorithm in airconditioner market segmentation. Expert Systems with Applications 36(3, Part 1):4437-4442.
8. Choi, S. Y., B. Choi, and H. Lee. 2006. Categorizing commercial products for customer oriented online retailing. Computers & Industrial Engineering 51(1):90-101. (Pubitemid 44472597)
9. Chun, J.-S., M.-K. Kim , H.-K. Jung, and S.-K. Hong. 1997. Shape optimization of electromagnetic devices using immune algorithm. IEEE Transactions on Magnetics 33(2):1876-1879. (Pubitemid 127826695)
10. de Castro, L. N., and F. J. Von Zuben. 2000. The clonal selection algorithm with engineering applications. In: Proceedings of GECCO-00, workshop on artificial immune systems and their applications, 36-37. Las Vegas, NV, USA.
11. de Castro, L. N., and F. J. Von Zuben. 2002. Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation 6(3): 239-251. (Pubitemid 34867248)
12. de Castro, L. N., and J. Timmis. 2002. Artificial immune sy stems: A new computational approach. London,UK: Springer-Verlag.
13. Forgy, E. W. 1965. Cluster analysis of multivariate data: Efficiency vs interpretability of classifications. Biometrics 21:768-769.
14. Graaff, A. J., and A. P. Engelbrecht. 2011. Using sequential deviation to dynamically determine the number of clusters found by a local network neighbourhood artificial immune system. Applied Soft Computing 11(2):2698-271 3.
15. Ha, S. H., and S. C. Park. 1998. Application of data mining tools to hotel data mart on the Intranet for database marketing. Expert Systems with Applications 15(1):1-31. (Pubitemid 128425918)
16. Hall, L. O., I. B. Ozyurt, and J. C. Bezdek. 1999. Clustering with a genetically optimized approach. IEEE Transactions on Evolutionary Computation 3(2):103-112.
17. Hamerly, G., and C. Elkan. 2003. Learning the k in k-means. In: Proceedings of the 7th annual conference on advances in neural information processing systems (NIPS), 281-288. Cambridge, MA: MIT Press.
18. Han J.,M. Kamber. 2006 Data mining: concepts and techniques (2nd ed. San Francisco, CA: Morgan Kaufmann.
19. Hart, E., and P. Ross. 2001. Clustering moving data with amodified immune algorithm. In Proceedings of the evoworkshops on applications of evolutionary computin g , 394-403, ed. E. Boers. London, UK: Springer-Verlag. (Pubitemid 33261189)
20. Hosseini, S. M. S., A. Maleki, and M. R. Gholamian. 2010. Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications 37(7):5259-5264.
21. Hughes, A. M. 1994. Strategi c database marketing . Chicago, IL: Probus.
22. Jansen, T., and C. Zarges. 2011. Analyzing different variants of immune inspired somatic contiguous hypermutations. Theoretical Computer Science 412(6):517-533.
23. Kennedy, J., and R.C. Eberhart. 2001. Swarm intell igence. San Mateo, CA: Morgan Kaufmann.
24. Khilwani, N., A. Prakash, R. Shankar, and M. K. Tiwari. 2008. Fast clonal algorithm. Engineering Applications of Artificial Intelligence 21(1):106-128. (Pubitemid 350117449)
25. Kiang, M.Y., T. S. Raghu, and K. H.-M. Shang. 2000. Marketing on the Internet-who can benefit from an online marketing approach. Decision Support Systems 27(4):383-393. (Pubitemid 30567440)
26. Krishna, K., and M. Narasimha Murty. 1999. Genetic K-means algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 29(3):433-439.
27. Kuo, R. J., and F. J. Lin. 2008. Application of particle swarm optimization to reduce SMT setup time for industrial PC manufacturer in Taiwan. International Journal of Innovative Computing, Information and Control 6(8):3381-3400.
28. Kuo, R. J., H. S.Wang, T.-L. Hu, and S. H. Chou. 2005. Application of ant K-means on clustering analysis. Computers & Mathematics with Applications 50(10- 12):1709-1724. (Pubitemid 41774571)
29. Kuo, R., M. Wang, and T. Huang. 2011. An application o f particle swarm optimization algorithm to clustering analysis. Soft Computing-A Fusion of Foundations, Methodologies and Applications 15(3):533-542.
30. Le, H. H. 2008. Taguchi methods: Principles and practices of quality design. Taipei, Taiwan: Gau Lih.
31. Li, X., H. Xu, and Z. Cheng. 2008. One immune simplex particle swarm optimization and it's application. In Proceedings of the 4th International Conference on Natural Computation, 331-335.
32. Liang, Y.-H. 2010. Integration of data mining technologies to analyze customer value for the automotive maintenance industry. Expert Systems with Applications 37(12):7489-7496.
33. Liao, S.-H., Y.-J. Chen, and M.-Y. Deng. 2010. Mining customer knowledge for tourism new product development and customer relationship management. Expert Systems with Applications 37(6):4212-4223.
34. Lin, C. F., C. C. Wu, P. H. Yang, and T. Y. Kuo. 2009. Application of Taguchi method in light-emitting diode backlight design for wide color gamut displays. Journal of Display Technology 5(8):323-330.
35. Liu, D.-R., and Y.-Y. Shih. 2005. Integrating AHP and data mining for product recommendation based on customer lifetime value. Information and Management 42(3):387-400.
36. Ma, W., L. Jiao, and M. Gong. 2009. Immunodominance and clonal selection inspired multiobjective clustering. Progress in Natural Science 19(6):751-758.
37. Marcus, C. 1998. A practical yet meaningful approach to customer segmentation. Journal of Consumer Marketing 15(5):494-504. (Pubitemid 128446157)
38. Miranda, F. F., M. Kolberg, K. K. Andersson, C.-F.G.C. Geraldes, and A. Martínez. 2005. The active site residue tyrosine 325 influ ences iron binding and coupling efficiency in human phenylalanine hydroxylase. Journal of Inorganic Biochemistry 99:1320-1328. (Pubitemid 40726028)
39. Mu, Y., and A. Sheng. 2009. Evolutionary diagonal recurrent neural network with imp roved hybrid EP-PSO algorithm and its identification application. International Journal of Innovative Computing, Information and Control 5(6):1615-1624.
40. Nasraoui, O., C. Rojas, and C. Cardona. 2006. A frame work formining evolving trends in web data streams using dynamic learning and retrospective validation. Computer Networks 50(10):1488-1512. (Pubitemid 43669059)
41. Nunen, J. A. E. E. V., and R. O. Zuidwijk. 2004. E-enabled c losed-loop supply chains. California Management Review 46(2):40-54.
42. Pai, J.-C., and F.-M. Tu. 2011. The acceptance and use of customer relationship man agement (CRM) systems: An empirical study of distribution service industry in Taiwan. Expert Systems with Applications 38(1):579-584.
43. Shah, J., and M. Murtaza. 2005. Effective customer relationship management t hrough web services. Journal of Computer Information Systems 46(1):98-109. (Pubitemid 41539050)
44. Shi, Y., and R. C. Eberhart. 1998. Parameter selection in particle swarm optimization. In Proceedings of the international co nference on evolutionary programming VII , 591-600. New York, NY: Springer-Verlag. (Pubitemid 128118969)
45. Shukla, S. K., M. K. Tiwari, H.-D. Wan, and R. Shankar. 2010. Optimization of the supply chain network: Simulation, Taguchi, and psychoclonal algorithm embedded approach. Computers & Industrial Engineering 58(1):29-39.
46. Sotiropoulos, D. N., G. A. Tsihrintzis, A. Savvopoulos, and M. Virvou. 2006. A comparison of customer data clustering techniques in an e-shopping application. In Proceedings of 2nd international workshop on web personalization, recommender systems and intelligent user interfaces. Ireland.
47. Spengler, B. 1999. Eyes on the customer. Computerworld 33(1):60-62.
48. Taguchi, G., S. Chowdhury, and Y. Wu. 2005. Taguchi's quality engineering handbook. Hoboken, New Jersey: Wiley.
49. Tang, N., and V. R. Vemuri. 2005. An artificial immune system approach to document clustering. In Proceedings of the 2005 ACM symposium on applied computing , 918-922. Santa Fe, NM: ACM.
50. Twycross, J., and U. Aickelin. 2010. Information fusion in the immune system. Information Fusion 11(1):35-44.
51. Wang, D., and W.-Z. Lu. 2006. Forecasting of ozone level in time series using MLP model with a novel hybrid training algorithm. Atmospheric Environment 40(5):913-924. (Pubitemid 43035534)
52. Wu, I.-L., and C.-Y. Hung. x2009. A strategy-based process for effectively determining system requirements in eCRM development. Information and Software Technology 51(9):1308-1318.
53. Xu, R., and D. WunschII. 2005. Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3):645-678. (Pubitemid 40718010)
54. Younsi, R., and W. Wang. 2004. A new artificial immune system algorithm for clustering. In Intelligent data engineering and automated learning-(IDEAL 2004), Lecture Notes in Computer Science 3177: 58-64,ed. Z. Yang, H. Yin, and R. Everson. Berlin, Heidelberg: Springer.
55. Yue, X., A. Abraham, Z.-X. Chi, Y.-Y. Hao, and H. Mo. 2007. Artificial immune system inspired behaviorbased anti-spam filter. Soft Computing 11(8):729-740. (Pubitemid 46437686)