5.1 Conclusions
The results of this research found three crisp markets and one fuzzy market, the study also mining two association rules and two fuzzy association rules for retailers. The size of the cluster A is 291(35%), the cluster B is 388(46%), the cluster C is 158(19%) and the fuzzy cluster F is 203(24.25%). However, the objective of this research is to propose a data mining approach for discovering customers marketing and purchasing rules, and also to integrate the fuzzy clustering and supervised-apriori-algorithm. Hence, the fuzzy association rules can be applied in e-business marketing systems to filter the valuable fuzzy target market and to understand shopping behaviors of the customers.
The scholar contribution of this study is to provide a data mining approach to integrate the fuzzy c-means and supervised apriori algorithm to generate the association rules in fuzzy mode. Via this case study, purchasing behaviors of the coffee shops’ customers in Taiwan have been shown. The industry of the coffee shops of Taiwan can easily apply their marketing projects on the most two market shares (cost and environmental considerations) for increasing royalties of customers to retain existed consumers, as well as attracting new consumers. Furthermore, for enhancing the Market C: Service Consideration, Pynnönen et al. (2014) suggested that businesses should offer full integrated service solutions for customers. Thus, the coffee shops in Taiwan can provide more free services for attracting more young customers, such as free Internet service in stores and free information and ordering services via smart phone apps.
