Abstract
This study proposes novel clustering algorithm based on genetic algorithms (GAs) to carry out a segmentation of the online shopping market effectively. In general, GAs are believed to be effective on NP-complete global optimization problems and they can provide good sub-optimal solutions in reasonable time. Thus, we believe that a clustering technique with GA can provide a way of finding the relevant clusters. This paper applies GA-based K-means clustering to the real-world online shopping market segmentation case for personalized recommender systems. In this study, we compare the results of GA-based K-means to those of traditional K-means algorithm and self-organizing maps. The result shows that GA-based K-means clustering may improve segmentation performance in comparison to other typical clustering algorithms.
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References
Bradley, P.S., Fayyad, U.M.: Refining Initial Points for K-means Clustering. In: Proc. of the 15th International Conference on Machine Learning, pp. 91–99 (1998)
Dibb, S., Simkin, L.: The Market Segmentation Workbook: Target Marketing for Marketing Managers, Routledge, London (1995)
Gehrt, K.C., Shim, S.: A Shopping Orientation Segmentation of French Consumers: Implications for Catalog Marketing. J. of Interactive Marketing 12(4), 34–46 (1998)
Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the Theory Neural Computation. Addison-Wesley, Reading (1991)
Kehoe, C., Pitkow, J., Rogers, J.D.: Ninth GVU.s WWW User Survey (1998), http://www.gvu.gatech.edu/user_surveys/survey-1998-04/
Kim, K.: Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting. Applied Intelligence (2004) (forthcoming)
Kohonen, T.: Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics 43(1), 59–69 (1982)
Michaud, P.: Clustering Techniques. Future Generation Computer Systems 13, 135–147 (1997)
Shin, H.W., Sohn, S.Y.: Segmentation of Stock Trading Customers According to Potential Value. Expert Systems with Applications 27(1), 27–33 (2004)
Shin, K.S., Han, I.: Case-Based Reasoning Supported by Genetic Algorithms for Corporate Bond Rating. Expert Systems with Applications 16, 85–95 (1999)
Velido, A., Lisboa, P.J.G., Meehan, K.: Segmentation of the On-Line Shopping Market using Neural Networks. Expert Systems with Applications 17, 303–314 (1999)
Wong, F., Tan, C.: Hybrid Neural, Genetic and Fuzzy Systems. In: Deboeck, G.J. (ed.) Trading On The Edge, pp. 243–261. Wiley, New York (1994)
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Kim, Kj., Ahn, H. (2005). Using a Clustering Genetic Algorithm to Support Customer Segmentation for Personalized Recommender Systems. In: Kim, T.G. (eds) Artificial Intelligence and Simulation. AIS 2004. Lecture Notes in Computer Science(), vol 3397. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30583-5_44
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DOI: https://doi.org/10.1007/978-3-540-30583-5_44
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