计算机与数字工程2019，Vol.47Issue(7)：1596-1600,5.DOI:10.3969/j.issn.1672-9722.2019.07.008
改进K-Means聚类算法在停车用户价值分群中的应用
Application of Improved K-Means Clustering Algorithm in Parking User Value Clustering
摘要
Abstract
With the development of information technology and Internet era,many enterprises take user relationship manage?ment as the focus of marketing. The clustering of user value is the key indicator of quantitative user relationship management. In this paper,it is taken business users parking data as the starting point,and in the traditional customer relationship management analysis based on RFM model,combined with the parking business requirements,parameters are reconstructed and analyzed,FLCPA Para?metric model is constructed. And based on the traditional K-Means clustering algorithm,a new method is put forward to determine the optimal number of clustering algorithm K-Means,which can effectively identify users with different values,and ultimately achieve customer value clustering. It can help for enterprises to develop targeted and personalized marketing strategy.关键词
价值分群/FLCPA模型/K-Means聚类算法/最优聚类数Key words
value grouping/FLCPA model/K-Means algorithm/optimal number of clusters分类
信息技术与安全科学引用本文复制引用
李向荣,范福海,孟向海..改进K-Means聚类算法在停车用户价值分群中的应用[J].计算机与数字工程,2019,47(7):1596-1600,5.
