电子商务客户忠诚度模型仿真研究

发布时间：2017-12-28 18:01

本文关键词：电子商务客户忠诚度模型仿真研究　出处：《计算机仿真》2016年01期 　论文类型：期刊论文

【摘要】：由于电子商务网站发展迅速,竞争激烈,提高客户忠诚度维护成为电子商务网站抢占、保有市场的重要途径。传统K均值算法作为一种硬聚类算法,在电子商务网站客户忠诚度客户忠诚度挖掘过程中存在数据划分武断、准确率较低等问题,难以在海量客户数据中发现潜在的有价值信息,为了解决上述问题,采用模糊数学理论改进k-means算法,提出模糊kmeans算法,利用隶属度关系对客户数据进行有效的聚类分析,实验结果表明,改进算法能够提高客户忠诚度挖掘准确度,与RFM模型、用户兴趣度模型算法相比,上述算法具有较好的鲁棒性,为保证网络商务忠诚度提供了依据。
[Abstract]:Due to the rapid development of e-commerce websites and fierce competition, the improvement of customer loyalty has become an important way to seize and retain the market of e-commerce websites. The traditional K means algorithm as a hard clustering algorithm in e-commerce website customer loyalty and customer loyalty are divided, arbitrary data mining low accuracy problems in the process, difficult to find in the massive customer data in potentially valuable information, in order to solve the above problems, the improved k-means algorithm based on fuzzy mathematics theory, the fuzzy kmeans algorithm, effective analysis of customer data clustering using membership relations, the experimental results show that the improved algorithm can improve customer loyalty mining accuracy, compared with the RFM model, user interest model algorithm, the algorithm has good robustness, provides the basis for the network to ensure the loyalty of business.
【作者单位】：北京工商大学计算机与信息工程学院;
【分类号】：F724.6;TP393.092
【正文快照】： l引言云计算、移动社交、数据挖掘等技术促进了电子商务网站的蓬勃发展,已经诞生了京东、天猫、蘑菇街、苏宁云商、国美商城等电子商务网站,方便人们购物,提高了人们生活的便利性。电子商务网站在发展运行中,已经积累了海量的消费者购买数据,可以采用数据挖掘算法发现大数据

【相似文献】