  Product Bundling and offering products to customers is of critical importance
in retail marketing. In general, product bundling and offering products to
customers involves two main issues, namely identification of product taste
according to demography and product evaluation and selection to increase sales.
The former helps to identify, analyze and understand customer needs according
to the demo-graphical characteristics and correspondingly transform them into a
set of specifications and offerings for people. The latter, concerns with how
to determine the best product strategy and offerings for the customer in
helping the retail market to improve their sales. Existing research has focused
only on identifying patterns for a particular dataset and for a particular
setting. This work aims to develop an explicit decision support for the
retailers to improve their product segmentation for different settings based on
the people characteristics and thereby promoting sales by efficient knowledge
discovery from the existing sales and product records. The work presents a
framework, which models an association relation mapping between the customers
and the clusters of products they purchase in an existing location and helps in
finding rules for a new location. The methodology is based on the integration
of popular data mining approaches such as clustering and association rule
mining. It focuses on the discovery of rules that vary according to the
economic and demographic characteristics and concentrates on marketing of
products based on the population.
