Application of Improved K-Means Clustering Algorithm in Parking User Value Clustering
LI Xiangrong
FAN Fuhai
MENG Xianghai
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.
Keywords：value groupingFLCPA modelK-Means algorithmoptimal number of clusters
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 1596-1600 )