By Nicolaas Du Plessis
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Contrastingly, the consumers of cluster 14 (represented by O14) pay less attention to nutrition. They are less concerned about food products with additives or preservatives (item 44). Health-oriented food consumption behavior is less important in their lives. Moreover, they put emphasis on low prices (item 38). The origin and quality of the foods are secondary (item 27, 35, and 42). Consequently, we can label this cluster as the uninvolved consumers. Cluster 3 (represented by ^3) comprises consumers who have a preference for natural, unprocessed foods (items 35, 42, and 44).
FRANC, V. and HLAVAC, V. (2004): Statistical Pattern Recognition Toolbox for Matlab. 8. MERCER, J. (1909): Functions of positive and negative type and their connection with the theory of integral equations. Philosophical Transactions Royal Society London, A209, 415-446. MIKA, S. (2002): Kernel Fisher discriminants. D. Thesis, Technical University of Berlin, Berlin, Germany. MORGERA, S. D. (1985). Information theoretic covariance complexity and its relation to pattern recognition. IEEE Transactions on System^s, Man, and Cybernetics, SMC-15, 608-619.
Moscow, 144-158 (in Russian). H. (1971): An Analysis of Complexity. Mathematical Centre Tracts, Amsterdam, 35. VAPNIK, V. (1995): The Nature of Statistical Learning Theory, Springer-Verlag, New York. Growing Clustering Algorithms in Market Segmentation: Defining Target Groups and Related Marketing Communication Reinhold Decker, Soren W. Scholz, and Ralf Wagner Business Administration and Marketing, Bielefeld University, Universitatsstral^e 25, D-33615, Germany A b s t r a c t . This paper outlines innovative techniques for the segmentation of consumer markets.