An algorithm for computing the transitive closure of a fuzzy similarity matrix
Fuzzy Sets and Systems
A conceptual version of the K-means algorithm
Pattern Recognition Letters
A model of consensus in group decision making under linguistic assessments
Fuzzy Sets and Systems
An approximation algorithm for clustering graphs with dominating diametral path
Information Processing Letters
Integration of self-organizing feature map and K-means algorithm for market segmentation
Computers and Operations Research
Logic-oriented fuzzy clustering
Pattern Recognition Letters
An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
Comparison of clustering methods for clinical databases
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
A clustering method to identify representative financial ratios
Information Sciences: an International Journal
The optimal approximation of fuzzy tolerance relation
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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).