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Cluster Analysis: For Segmenting the Population

  • J. P. Verma
Chapter

Abstract

Market analysts are always in search of strategies responsible for buying behavior. The whole lot of customers can be grouped on the basis of their past buying behavior patterns. This segmentation of customers helps analysts in developing marketing strategy for different products in different segments of customers. These segments are developed on the basis of buying behavior of the customers in such a way so that the individuals in the segments are more alike but the individuals in different segments differ to a great extent in their characteristics. The concept of segmenting may be used to club different television serials into homogeneous categories on the basis of their characteristics. An archaeological surveyor’s may like to cluster different idol excavated from archaeological digs into the civilizations from which they originated. These idols may be clustered on the basis of their physical and chemical parameters to identify their age and civilization to which they belong. Doctors may diagnose a patient for viral infection and determine whether distinct subgroups can be identified on the basis of a clinical checklist and pathological tests. Thus, several examples in different fields may require segmenting the subjects on the basis of their behavior pattern for developing an appropriate strategy for treating the subjects in different segments. Similarly, segmenting may be done for the objects based on their similarity of features and characteristics. Such segmenting of objects may be useful for making a policy decision. For instance, all the cars can be segmented into low, middle, and large groups depending upon some of their features like engine power, price, seating capacity, luggage capacity, and fuel consumption. Different policy may be adopted to promote these segments of vehicle by the authorities.

Keywords

Cluster Analysis Cluster Center Cluster Solution Cluster Membership Agglomerative Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Research and Advanced StudiesLakshmibai National University of Physical EducationGwaliorIndia

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