Article 1, Volume 3, Issue 1, Summer 2016, Page 1-9 


1* Department of Industrial Engineering, Faculty of Engineering, University of Tehran,

2 Department of Industrial Engineering, Faculty of Engineering, University of Tehran,

3 Department of Industrial Engineering, Faculty of Engineering, University of Tehran,


An increasing demand of dairy product worldwide gives a good opportunity for dairy industries; and under the blows of globalization and pierce of technology to various corners, the competition amongst dairy industries are getting tougher and harder day-by-day. Dairy companies in order to sustain their competitiveness should segment and analyze customer attitudes and behaviors. This paper segments the customers in the Urmia city of Iran based on their attention and important degree of dairy products brand. The various customer characteristics such as age, income, gender and ethnicity are used for segmenting and clustering. Several dairy products such as cheese, butter, milk, chocolate milk, yogurt, Doogh and ice cream are utilized in this paper and for each of them a separate clustering has been performed. In this paper, the hierarchical approach is used to find the initial number of clustering, then by utilizing the K-mean algorithm the best cluster is calculated.


Austin, S. B., Melly, S. J., Sanchez, B. N., Patel, A., Buka, S. and Gortmaker, S. L. (2005). Clustering of fast-food restaurants around schools: a novel application of spatial statistics to the study of food environments. American Journal of Public Health, Vol. 95, No. 9, 1575-1581.

Bennett, P. D. (1995). Dictionary of marketing terms, Contemporary Books.

Buck, C., Börnhorst, C., Pohlabeln, H., Huybrechts, I., Pala, V., Reisch, L. and Pigeot, I. (2013). Clustering of unhealthy food around German schools and its influence on dietary behavior in school children: a pilot study. International Journal of Behavioral Nutrition and Physical Activity, Vol. 10, No. 1, 65.

Chang, D.-X., Zhang, X.-D. and Zheng, C.-W. (2009). A genetic algorithm with gene rearrangement for K-means clustering. Pattern Recognition, Vol. 42, No. 7, 1210-1222.

Chen, C.-Y. and Ye, F. (2004). Particle swarm optimization algorithm and its application to clustering analysis. Networking, Sensing and Control, 2004 IEEE International Conference on, IEEE.

Chifu, V. R., Pop, C. B., Salomie, I. and Aczel, A. (2015). CLUSTERING FOOD RECIPES USING A FLOWER POLLINATION-BASED METHOD. Acta Technica Napocensis, Vol. 56, No. 4, 42.

Everitt, B., Landau, S. and Leese, M. (2001). Cluster Analysis Arnold. A member of the Hodder Headline Group, London, No.

González, R. and Tou, J. (1974). Pattern recognition principles. Applied Mathematics and Computation. Reading (MA): Addison-Wesley, No.

Joo, S., Ju, S. and Chang, H. (2015). Comparison of fast food consumption and dietary guideline practices for children and adolescents by clustering of fast food outlets around schools in the Gyeonggi area of Korea. Asia Pacific journal of clinical nutrition, Vol. 24, No. 2, 299.

Kotler, P. and Turner, R. E. (1979). Marketing Management: analysis, planning, and control, Prentice-Hall Englewood Cliffs, NJ.

Li, H.-C., Ko, W.-M. and Tung, H.-W. (2007). Food Clustering Analysis for Personalized Food Replacement. Fuzzy Information Processing Society, 2007. NAFIPS’07. Annual Meeting of the North American, IEEE.

Lingling, X. (2010). Applying grey relation clustering and PCA to performance evaluation of vendors in fresh milk supply chain. Logistics Systems and Intelligent Management, 2010 International Conference on, IEEE.

Menrad, K. (2003). Market and marketing of functional food in Europe. Journal of food engineering, Vol. 56, No. 2, 181-188.

Michael, W. B. (2007). Survey of Text Mining: Clustering, Classification and Retrieval, Published by Springer, USA.

Mollet, B. and Rowland, I. (2002). Functional foods: at the frontier between food and pharma. Current Opinion in Biotechnology, Vol. 13, No. 5, 483-485.

Nunnally, J. (1978). C.(1978). Psychometric theory, New York: McGraw-Hill.

Roberfroid, M. B. (2000). A European consensus of scientific concepts of functional foods. Nutrition, Vol. 16, No. 7, 689-691.

Rodenburg, G., Oenema, A., Pasma, M., Kremers, S. P. and van de Mheen, D. (2013). Clustering of food and activity preferences in primary school children. Appetite, Vol. 60, No. 123-132.

Smith, W. R. (1956). Product differentiation and market segmentation as alternative marketing strategies. The Journal of Marketing, No. 3-8.

Wedel, M. and Kamakura, W. A. (2002). Introduction to the special issue on market segmentation. Intern. J. of Research in Marketing, Vol. 19, No. 181-183.

Windham, C. T., Windham, M. P., Wyse, B. W. and Hansen, R. G. (1985). Cluster analysis to improve food classification within commodity groups. Journal of the American Dietetic Association, Vol. 85, No. 10, 1306-1314.

Xu-Wei, P., and Min, J. (2013). Wnbac: A Weighted Network Based Adaptive Clustering Algorithm for Spatial Objects. Information Technology Journal, Vol. 12, No. 23, 7849.

Xu, R., and Wunsch, D. (2005). Survey of clustering algorithms. Neural Networks, IEEE Transactions on, Vol. 16, No. 3, 645-678.

Young, J. (2000). Functional foods and the European consumer. SPECIAL PUBLICATION-ROYAL SOCIETY OF CHEMISTRY, Vol. 248, No. 75-81.


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