Integrating data mining and rough set for customer group-based discovery of product configuration rules

Abstract

Product configuration design is of critical importance in design for mass customization. This paper will investigate two important issues in configuration design. The first issue is requirement configuration and a dependency analysis approach is proposed and implemented to link customer groups with clusters of product specifications. The second issue concerns the engineering configuration and it is modelled as an association relation between clusters of product specifications and configuration alternatives. A novel methodology and architecture are proposed for accomplishing the two configuration tasks and bridging the gap between them. This methodology is based on integration of popular data mining approaches (such as fuzzy clustering and association rule mining) and variable precision rough set. It focuses on the discovery of configuration rules from the purchased products according to customer groups. The proposed methodology is illustrated with a case study of an electrical bicycle.

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