A Study of Fuzzy Clustering Algorithm for CRM Customers Classification

• Main Authors: OU,GUANG-RONG, 歐光榮
• Other Authors: LIU,CHIA-HWA
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/97276412924726451733

碩士 === 醒吾技術學院 === 資訊科技應用研究所 === 97 === Accurate customer classification is the foundation of efficient relationship management between customs and enterprise. Customer classification is performed to divide the customs into different sets according to the attributes of customs, and prediction of purchasing pattern possible, which will help to establish one-to-one service system and further difference-oriented custom management. However, the factors involved in the customer classification are of large amount and fuzzy, the criterions are also different for different goals. Therefore, a general method for customer classification is not possible. In this paper, we proposed a customer classification model based on fuzzy ISODATA clustering in the CRM system, which extract the features of customs and introduce the concept of soft clustering membership degree. Compared with the hard clustering membership degree, this new method can describe the characteristics of customs. From the starting value selection method in the Fuzzy ISODATA algorithm, used the method of maximal matrix element to ascertain the number of classification, the theoretical analysis of repeated test, and finally, the improved fuzzy ISODATA algorithm is obtained. The algorithm reduced sensitivity to the starting value. The algorithm can highly effective clustering analyze and obtains a stable result, so it presents an efficient way of improving the contribution value of custom service.