| 基于半监督谱聚类集成的售后客户细分 |
| |
| 引用本文： | 杨静雅,孙林夫,吴奇石.基于半监督谱聚类集成的售后客户细分[J].计算机工程与应用,2020,56(2):266-271. |
| |
| 作者姓名： | 杨静雅 孙林夫 吴奇石 |
| |
| 作者单位： | 西南交通大学 信息科学与技术学院，成都 610031 |
| |
| 摘 要： | 根据汽车售后服务客户细分的目的，以及保修期内客户对车辆的保养情况，构建了RFMD客户细分指标模型。针对聚类集成算法能充分挖掘数据集的内在结构，以及半监督学习思想利用先验知识指导聚类的优势，将半监督谱聚类集成（SSSCE）算法应用于售后服务客户细分。与谱聚类（SC）算法和谱聚类集成（SCE）算法相比，SSSCE算法的客户细分结果较优。对用SSSCE算法细分得到的客户群进行特征分析，并给出相应的保养指导策略。
|
| 关 键 词： | 汽车售后服务 客户细分 RFMD模型 半监督谱聚类集成（SSSCE）算法 保养策略 |
After-Sales Customer Segmentation Based on Semi-Supervised Spectral Clustering Ensemble |
| |
| YANG Jingya,SUN Linfu,WU Qishi.After-Sales Customer Segmentation Based on Semi-Supervised Spectral Clustering Ensemble[J].Computer Engineering and Applications,2020,56(2):266-271. |
| |
| Authors: | YANG Jingya SUN Linfu WU Qishi |
| |
| Affiliation: | School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China |
| |
| Abstract: | According to the purpose of automotive after-sales service customer segmentation and the maintenance of the vehicle during the warranty period, the RFMD customer segmentation indicator model is constructed. Aiming at the advantages that the clustering ensemble algorithm can fully exploit the internal structure of the data set, and the semi-supervised learning idea uses the prior knowledge to guide the clustering, the Semi-Supervised Spectral Clustering Ensemble（SSSCE） algorithm is applied to after-sales customer segmentation. Compared with the Spectral Clustering（SC） algorithm and the Spectral Clustering Ensemble（SCE） algorithm, the SSSCE algorithm has better customer segmentation results. Finally, the customer group segmented by SSSCE algorithm is analyzed for characteristics, and the corresponding maintenance guidance strategy is given. |
| |
| Keywords: | automotive after-sales service customer segmentation RFMD model Semi-Supervised Spectral Clustering Ensemble（SSSCE） algorithm maintenance strategy |
| 本文献已被 万方数据 等数据库收录！ |
| 点击此处可从《计算机工程与应用》浏览原始摘要信息 |
|
点击此处可从《计算机工程与应用》下载全文 |
