A clustering method based on fuzzy equivalence relation for customer relationship management

In real world, customers commonly take relevant attributes into consideration for the selection of products and services. Further, the attribute assessment of a product or service is often presented by a linguistic data sequence. To partition these linguistic data sequences of customers’ assessment on a product or service, a proper clustering method is essential and proposed in this paper. In the clustering method, the linguistic data sequences are presented by fuzzy data sequences and a fuzzy compatible relation is first constructed to present the binary relation between two data sequences. Then a fuzzy equivalence relation is derived by max–min transitive closure from the fuzzy compatible relation. Based on the fuzzy equivalence relation, the linguistic data sequences are easily classified into clusters. The clusters representing the selection preferences of different customers on the product or service will be the foundation of developing customer relationship management (CRM).