基于直觉模糊C-均值的客户聚类和识别方法

客户聚类和识别是大规模客户化生产中产品/服务快速有效设计的基础.考虑客户需求信息的不确定性,提出了基于直觉模糊C-均值的客户聚类算法.针对传统基于欧式距离的C-均值聚类方法无法计算直觉模糊数组间距离的缺点,采用直觉模糊交叉熵方法处理算法中的距离计算问题.同时,直觉模糊交叉熵还用来计算新客户和各客户类间的偏好相似度,进行客户识别.最后以某工程机械企业服务开发中的客户聚类和识别为例,验证了所提方法的有效性.

Customer Clustering and Pattern Identification Approach Based on Vague C-means

In the mass customization production, customer clustering and identification are the basis of quick and effective product/service design.Considering the uncertainty of customer requirements, a customer clustering and pattern identification approach based on vague C-means was proposed.Aiming at the problem that the traditional fuzzy C-means based on Euclidean distance cannot deal with the distance between vague sets, a vague cross-entropy approach was adopted to deal with the distance calculating problem in the C-means clustering algorithm.At the same time, the vague cross-entropy was also applied in calculating the similarity between new customer and different customer groups, and then the customer identification was realized.Finally, a case study of customer clustering and identification in a mechanical company's service development was presented to illustrate the effectiveness of the proposed approach.