Abbas Keramati - Department of Industrial Engineering, Faculty of Engineering, University of Tehran
Mehdi Keshavarz - Department of Industrial Engineering, Faculty of Engineering, University of Tehran
Abolghasem Yousefi-Babadi - epartment 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.

Dairy Product, Brand, Clustering, K-Mean Algorithm, Urmia