Rough clustering of destination image data using an evolutionary algorithm

Journal of Travel and Tourism Marketing

Volumn 21, Issue 4, 2006, Pages 121-137

  Voges, Kevin E ^a  

^a UNIVERSITY OF CANTERBURY   (New Zealand)

Author keywords

Cluster analysis; Evolutionary algorithms; Rough clustering; Rough sets

Indexed keywords

EID: 34548536251     PISSN: 10548408     EISSN: None     Source Type: Journal    
DOI: 10.1300/J073v21n04_09     Document Type: Review

References (111)

1. Alon, I., Qi, M., & Sadowski, R. (2001). Forecasting aggregate retail sales: A comparison of artificial neural networks and traditional methods. Journal of Retailing and Consumer Services, 8 (3), 147-156.
2. Amaravadi, C. S., Samaddar, S., & Dutta, S. (1995). Intelligent marketing information systems: Computerized intelligence for marketing decision making. Marketing Intelligence and Planning, 13 (2), 4-13.
3. Anand, T., & Kahn, G. (1993). Focusing knowledge-based techniques on market analysis. IEEE Expert, August 1993, 19-24.
4. Bäch, T. (1996). Evolutionary algorithms in theory and practice: Evolution strategies, evolutionary programming, genetic algorithms. New York: Oxford University Press.
5. Beverland, M., & Lindgreen, A. (2002). Using country of origin in strategy: The importance of context and strategic action. Journal of Brand Management, 10 (2), 147-167.
6. Beynon, M. J., Curry, B., & Morgan, P. (2001). Knowledge discovery in marketing: An approach through rough set theory. European Journal of Marketing, 35 (7/8), 915-935.
7. Beynon, M. J., & Peel, M. J. (2001). Variable precision rough set theory and data discretisation: An application to corporate failure prediction. Omega, 29, 561-576.
8. Bhattacharyya, S. (2003). Evolutionary computation for database marketing. Journal of Database Management, 10(4), 343-352.
9. Biethahn, J., & Nissen, V. (Eds.). (1995). Evolutionary algorithms in management applications. Berlin: Springer-Verlag.
10. Bonn, M. A., Joseph, S. M., & Dai, M. (2005). International versus domestic visitors: An examination of destination image perceptions. Journal of Travel Research, 43 (3), 294-301.
11. Boo, S., & Busser, J. A. (2005). The hierarchical influence of visitor characteristics on tourism destination images. Journal of Travel and Tourism Marketing, 19 (4), 55-68.
12. Callan, R. (2003). Artificial intelligence. Basingstoke, England: Palgrave Macmillan.
13. Cheng, R., Gen, M., & Tsujimura, Y. (1996). A tutorial survey of job-shop scheduling problems using genetic algorithms: Part I. Representation. Computers and Industrial Engineering, 30 (4), 983-997.
14. Cheng, R., Gen, M., & Tsujimura, Y. (1999). A tutorial survey of job-shop scheduling problems using genetic algorithms: Part II. Hybrid genetic search strategies. Computers and Industrial Engineering, 37 (1-2), 51-55.
15. Chiou, J-S. (2003). The impact of country of origin on pretrial and posttrial product evaluations: The moderating effect of consumer expertise. Psychology and Marketing, 20 (10), 935-954.
16. Coello, C. A. (2000). An updated survey of GA-based multiobjective optimization techniques. ACM Computing Surveys, 32 (2), 109-143.
17. Cordón, O., Herrera-Viedma, E., López-Pujalte, C., Luque, M., & Zarco, C. (2003). A review on the application of evolutionary computation to information retrieval. International Journal of Approximate Reasoning, 34 (2-3), 241-264.
18. Darwin, C. (1859:2003). On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life. Peterborough: Broadview Press
19. Davis, L. (Ed.) (1991). Handbook of genetic algorithms. New York: Van Nostrand Reinhold.
20. De Castro, L. N., & Von Zuben, F. J. (Eds.) (2005). Recent developments in biologically inspired computing. Hershey, PA: Idea Group.
21. Dimitras, A. I., Slowinski, R., Susmaga, R., & Zopounidis, C. (1999). Business failure prediction using rough sets. European Journal of Operational Research, 114, 263-280.
22. do Prado, H. A., Engel, P. M., & Filho, H. C. (2002). Rough clustering: An alternative to find meaningful clusters by using the reducts from a dataset. In J. J. Alpigini, J. F. Peters, A. Skowron, & N. Zhong (Eds.). Rough Sets and Current Trends in Computing, Third International Conference RSCTC 2002 LNCS 2475 (pp. 234-238). Berlin: Springer-Verlag.
23. Engelbrecht, A. P. (2002). Computational intelligence: An introduction. Chichester, England: John Wiley and Sons.
24. Fernandes, E. (2002). Polishing the country's image: The economic effect of a 15% decline in the number of visitors prompts a campaign to relaunch India as a holiday destination. Financial Times, September 23 2002, p.2.
25. Fleming, P. J., & Purshouse, R. C. (2002). Evolutionary algorithms in control systems engineering: A survey. Control Engineering Practice 10(11), 1223-1241.
26. Fogel, D. B. (1995). Evolutionary computation: Toward a new philosophy of machine intelligence. New York: IEEE Press.
27. Fogel, L. J., Owens, A. J., & Walsh, M. J. (1966). Artificial intelligence through simulated evolution. New York: Wiley.
28. Forrest, S. (1988). Implementing semantic network structures using the classifier system. In J. J. Grefenstette (Ed.) Proceedings of the First International Conference on Genetic Algorithms and Their Applications (pp. 24-44). Hillsdale, NJ: Lawrence Erlbaum Associates.
29. Gen, M., & Kim, J. R. (1999). GA-based reliability design: State-of-the-art survey. Computers and Industrial Engineering, 37(1-2), 151-155.
30. Goh, C., & Law, R. (2003). Incorporating the rough sets theory into travel demand analysis. Tourism Management, 24, 511-517.
31. Golan, R., & Ziarko, W. (1995). A methodology for stock market analysis utilizing rough sets theory. In Proceedings IEEE/IAFE 1995 Conference on Computational Intelligence in Financial Engineering (pp. 32-40). New York: IEEE.
32. Goldberg, D. E. (1989a). Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.
33. Goldberg, D. E. (1989b). Sizing populations for serial and parallel genetic algorithms. In J. D. Schaffer (Ed.) Proceedings of the Third International Conference on Genetic Algorithms (pp. 70-79). San Mateo, CA: Morgan Kaufman.
34. Greco, S., Matarazzo, B., & Slowinski, R. (2000). Extension of the rough set approach to multicriteria decision support. INFOR, 38 (3), 161-195.
35. Grzymala-Busse, J. W. (1997). A new version of the rule induction system LERS. Fundamenta Informaticae, 31, 27-39.
36. Grzymala-Busse, J. W., & Zou, X. (1998). Classification strategies using certain and possible rules. In L. Polkowski & A. Skowron (Eds. Rough Sets and Current Trends in Computing: First International Conference, RSCTC98. LNCS 1424 (pp. 37-44). Berlin: Springer.
37. Gunn, C. (1972). Vacationscape. Austin, TX: Bureau of Business Research, University of Texas.
38. Hair, J. E., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). London: Prentice-Hall International.
39. Hankinson, G. (2005). Destination brand images: A business tourism perspective. Journal of Services Marketing, 19 (1), 24-32.
40. Hanlan, J., & Kelly, S. (2005). Image formation, information sources and an iconic Australian tourist destination. Journal of Vacation Marketing, 11 (2), 163-177.
41. Höhfeld, M. (1996). Industrial applications of evolutionary algorithms at Siemens AG. In J. T. Alander (Ed.) Proceedings of the Second Nordic Workshop on Genetic Algorithms and their Applications. Vaasa, Finland: University of Vaasa.
42. Holland, J. H. (1975). Adaptation in natural and artificial systems Ann Arbor, MI: The University of Michigan Press (Second Edition 1992).
43. Hosany, S., Ekinci, Y., & Uysal, M. (2006). Destination image and destination personality: An application of branding theories to tourism places. Journal of Business Research, 59, 638-642.
44. Huang, C-C., & Tseng, T-L. (2004). Rough set approach to case-based reasoning application. Expert Systems with Applications, 26, 369-385.
45. Huang, S. H., & Zhang, H-C. (1995). Neural-expert hybrid approach for intelligent manufacturing: A survey. Computers in Industry, 26(2), 107-126.
46. Hunt, J. D. (1975). Image as a factor in tourism development. Journal of Travel Research, 13 (4), 1-7.
47. Hurley, S., Moutinho, L., & Stephens, N. M. (1995). Solving marketing optimization problems using genetic algorithms. European Journal of Marketing, 29 (4), 39-56.
48. Ivakhnenko, A. G., & Ivakhnenko, N. A. (1974). Long-term prediction by GMDH algorithms using the unbiased criterion and the balance-of-variables criterion. Soviet Automatic Control, 7 (4), 40-45.
49. Kim, S. S., Guo, Y., & Agrusa, J. (2005). Preference and positioning analyses of overseas destinations by mainland Chinese outbound pleasure tourists. Journal of Travel Research, 44 (2), 212-220.
50. Klopotek, M., & Wierzchon, S. T. (1998). A new qualitative rough-set approach to modeling belief functions. In L. Polkowski & A. Skowron (Eds.) Rough Sets and Current Trends in Computing: First International Conference, RSCTC98. Lecture Notes in Computer Science, Vol. 1424 (pp. 346-354). Berlin: Springer.
51. Konecnik, M. (2005). Slovenia as a tourism destination: Differences in image evaluations perceived by tourism representatives from closer and more distant markets. Economic and Business Review for Central and South-Eastern Europe, 7 (3), 261-282.
52. Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection. Cambridge, MA: MIT Press.
53. Kuo, R. J. (2001). A sales forecasting system based on fuzzy neural network with initial weights generated by genetic algorithm. European Journal of Operational Research, 129, 496-517.
54. Li, S. (2000). The development of a hybrid intelligent system for developing marketing strategy. Decision Support Systems, 27, 395-409.
55. Li, S., Davies, B., Edwards, J., Kinman, R., & Duan, Y. (2002) Integrating group Delphi, fuzzy logic and expert systems for marketing strategy development: The hybridization and its effectiveness. Marketing Intelligence and Planning, 20 (4/5), 273-284.
56. Liefeld, J. P. (2004). Consumer knowledge and use of country-of-origin information at the point of purchase. Journal of Consumer Behaviour 4 (2), 85-95.
57. Lin, T. Y., & Cercone, N. (Eds.) (1997). Rough sets and data mining: Analysis of imprecise data. Boston: Kluwer.
58. Lingras, P. (2001). Unsupervised rough set classification using GAs. Journal of Intelligent Information Systems, 16 (3), 215-228.
59. Lingras, P. (2002). Rough set clustering for web mining. In Proceedings of 2002 IEEE International Conference on Fuzzy Systems.
60. Lingras, P. J., & Yao, Y. Y. (1998). Data mining using extensions of the rough set model. Journal of the American Society for Information Science, 49 (5), 415-422.
61. Liu, S. S., & Johnson, K. F. (2005). The automatic country-of-origin effects on brand judgments. Journal of Advertising, 34 (1), 87-97.
62. Luger, G. F., & Stubblefield, W. A. (1998). Artificial intelligence: Structures and strategies for complex problem solving (3rd ed.). Reading, MA: Addison Wesley Longman.
63. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In L. M. Le Cam, & J. Neyman (Eds.) Proceedings of the Fifth Berkeley Symposium on Mathematics, Statistics and Probability, Volume 1 (pp. 281-298). Berkeley, CA: University of California Press.
64. Mazanec, J. (1992). Classifying tourists into market segments: A neural network approach. Journal of Travel and Tourism, 1 (1), 39-59.
65. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A proposal for the Dartmouth Summer Research Project on Artificial Intelligence. (August 31, 1955). Retrieved May 2, 2005, from http://www-formal.stanford.edu/jmc/hisory/dartmouth/dartmouth.html
66. McKee, T. E. (2000). Developing a bankruptcy prediction model via rough sets theory. International Journal of Intelligent Systems in Accounting, Finance and Management, 9 (3), 159-173.
67. Michalewicz, Z. (1996). Genetic algorithms + Data structures = Evolution programs. Berlin: Springer-Verlag.
68. Montgomery, D., Swinnen, G., & Vanhoof, K. (1997). Comparison of some AI and statistical classification methods for a marketing case. European Journal of Operational Research, 103, 312-325.
69. Morgan, N. J., Pritchard, A., & Piggott, R. (2003). Destination branding and the role of the stakeholders: The case of New Zealand. Journal of Vacation Marketing, 9 (3), 285-299.
70. Munakata, T. (1998). Fundamentals of the new artificial intelligence: Beyond traditional paradigms. New York: Springer-Verlag.
71. Negnevitsky, M. (2002). Artificial intelligence: A guide to intelligent systems. Harlow, NY: Pearson/Addison.
72. Nguyen, S. H. (2000). Regularity analysis and its applications in data mining. In L. Polkowski, S. Tsumoto & T. Y. Lin (Eds.) Rough set methods and applications: New developments in knowledge discovery in information systems (pp. 289-378). Heidelberg: Physica-Verlag.
73. Nissen, V., & Biethahn, J. (1995). An introduction to evolutionary algorithms. In J. Biethahn & V. Nissen (Eds.) Evolutionary algorithms in management applications (pp. 3-43). Berlin: Springer-Verlag,
74. Obenour, W., Lengfelder, J., & Groves, D. (2005). The development of a destination through the image assessment of six geographic markets. Journal of Vacation Marketing, 11 (2), 107-113.
75. Orlowska, E. (1998). Incomplete information: Rough set analysis. Heidelberg: Physica-Verlag.
76. Pal, S. K., & Skowron A. (Eds.) (1999). Rough-fuzzy hybridization: A new trend in decision making. Singapore: Springer.
77. Pawlak, Z. (1982). Rough sets. International Journal of Information and Computer Sciences, 11, 341-356
78. Pawlak, Z. (1984). Rough classification. International Journal of Man-Machine Studies, 20, 469-483,
79. Pawlak, Z. (1991). Rough sets: Theoretical aspects of reasoning about data. Boston: Kluwer.
80. Pawlak, Z. (2000). Rough sets and decision analysis. INFOR, 38 (3), 132-144.
81. Pawlak, Z. (2001). Rough sets and decision algorithms. In W. Ziarko & Y. Yao (Eds.) Rough Sets and Current Trends in Computing (Second International Conference, RSCTC2000) (pp. 30-45). Berlin: Springer.
82. Pawlak, Z., Wong, S. K. & Ziarko, W. (1988). Rough sets: Probabilistic versus deterministic approach. International Journal of Man-Machine Studies, 29, 81-95.
83. Pereira, A., Hsu, C. C., & Kundu, S. K. (2005). Country-of-origin image: Measurement and cross-national testing. Journal of Business Research 58 (1), 103-106.
84. Peters, J. F., & Skowron, A. (Eds.) (2004). Transactions on rough sets I. Berlin: Springer-Verlag.
85. Polkowski, L., & Skowron, A. (Eds.) (1998). Rough Sets and Current Trends in Computing (First International Conference, RSCTC98). Berlin: Springer.
86. Polkowski, L., Tsumoto, S. & Lin, T. Y. (Eds.) (2000). Rough set methods and applications: New developments in knowledge discovery in information systems. Heidelberg: Physica-Verlag.
87. Poole, D., Mackworth, A., & Goebel, R. (1998). Computational intelligence: A logical approach. New York: Oxford University Press.
88. Price, K. V., Storn, R. M., & Lampinen, J. A. (2005). Differential evolution: A practical approach to global optimisation. Berlin: Springer.
89. Rechenberg, I. (1973). Evolutionsstrategie. Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. problemata. Stuttgart: Fromman-Holzboog (Second Edition 1993).
90. Reeves, C. R. (1993). Using genetic algorithms with small populations. In S. Forrest (Ed.) Proceedings of the Fifth International Conference on Genetic Algorithms (pp. 92-99). San Mateo, CA: Morgan Kaufman.
91. Schwefel, H-P. (1977). Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie. Basel: Birkhaeuser.
92. Schwefel, H-P. (1995). Evolution and optimum seeking. New York: Wiley.
93. Shen, L., & Loh, H. T. (2004). Applying rough sets to market timing decisions. Decision Support Systems, 37, 583-597.
94. Silvestre, A. L., & Correia, A. (2005). A second-order factor analysis model for measuring tourists' overall image of Algarve, Portugal. Tourism Economics, 11 (4), 539-554.
95. Son, A. (2005). The measurement of tourist destination image: Applying a sketch map technique. International Journal of Tourism Research, 7 (4/5), 279-294.
96. Son, A., & Pearce, P. (2005). Multi-faceted image assessment: International students' views of Australia as a tourist destination. Journal of Travel and Tourism Marketing, 18 (4), 21-35.
97. Srinivisan, N., & Jain, S. C. (2003). Country of origin effect: Synthesis and future direction. In S. C. Jain (Ed.) Handbook of research in international marketing (pp. 458-476). Cheltenham, UK: Elgar.
98. Stateman, A. (2005). After the tsunami: The complexities of promoting areas damaged by disasters. Public Relations Tactics, 12 (2), 10.
99. Suh, C-K., Suh, E-H., & Lee, D-M. (1995). Artificial intelligence approaches in model management systems: A survey. Computers & Industrial Engineering, 28(2), 291-299.
100. Turing, A. (1948). Intelligent machinery. (Unpublished report) Retrieved May 2, 2005, from http://www.alanturing.net/turing_archive/ archive/1/132/L32-001.html).
101. Turing, A. (1950). Computing machinery and intelligence. Mind, 59, 433-460.
102. Verlegh, P. W. J., & Steenkamp, J-B. E M. (1999). A review and meta-analysis of country-of-origin research. Journal of Economic Psychology, 20 (5), 521-546.
103. Verlegh, P. W. J., Steenkamp, J-B. E. M., & Menlenberg, M. T. G. (2005). Country-of-origin effects in consumer processing of advertising claims. International Journal of Research in Marketing, 22 (2), 127-139.
104. Voges, K. E. (1997). Using evolutionary algorithm techniques for the analysis of data in marketing. Cyber-Journal of Sport Marketing, 1 (2), 66-82.
105. Voges, K. E., & Pope, N. K. Ll. (2004). Generating compact rough cluster descriptions using an evolutionary algorithm. In K. Deb et al. (Eds.) GECC02004: Genetic and Evolutionary Algorithm Conference-LNCS 3103 (pp. 1332-1333). Berlin: Springer-Verlag.
106. Voges, K. E. & Pope, N. K. Ll. (Eds.) (2006). Business applications and computational intelligence. Hershey, PA: Idea Group.
107. Voges, K. E., Pope, N. K. Ll., & Brown, M. R. (2002). Cluster analysis of marketing data examining on-line shopping orientation: A comparison of k-means and rough clustering approaches. In H. A. Abbass, R. A. Sarker, & C. S. Newton (Eds.) Heuristics and Optimization for Knowledge Discovery (pp. 207-224). Hershey, PA: Idea Group Publishing.
108. Wang, G., Liu, Q., Yao, Y., & Skowron, A. (Eds.) (2003). Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: Proceedings 9th International Conference, RSFDGrC 2003. New York: Springer.
109. Zhong, N., Skowron, A., & Ohsuga, S. (Eds.) (1999). New directions in rough sets, data mining, and granular-soft computing. Berlin: Springer.
110. Ziarko, W., & Shan, N. (1995). Discovering attribute relationships, dependencies and rules by using rough sets. In L. Hunter & B. D. Shriver (Eds.). Proceedings of the 28th Annual Hawaii International Conference on System Sciences (HICSS'95) (pp. 293-299). Los Alamitos, CA: IEEE Computer Society Press
111. Zopounidis, C., Slowinski, R., Doumpos, M., Dimitras, A. I., & Susmaga, R. (1999). Business failure prediction using rough sets. Fuzzy Economic Review, 4 (1), 3-33.