基于密度信息熵的K-Means算法在客户细分中的应用  

Application of K-Means Algorithm Based on DensityInformation Entropy in Customer Segmentation

作　　者：蒲晓川 黄俊丽 祁宁 宋长松[2] PU Xiaochuan;HUANG Junli;QI Ning;SONG Changsong(School of Information Engineering,Zunyi Normal University,Zunyi 563006,Guizhou Province,China;Graduate School of Management of Technology,Pukyong National University,Busan 48513,South Korea;School of Management,Zunyi Normal University,Zunyi 563006,Guizhou Province,China;School of Economics and Management,Hexi University,Zhangye 734000,Gansu Province,China)

机构地区：[1]遵义师范学院信息工程学院,贵州遵义563006 [2]国立釜庆大学技术管理学院,韩国釜山48513 [3]遵义师范学院管理学院,贵州遵义563006 [4]河西学院经济管理学院,甘肃张掖734000

出　　处：《吉林大学学报（理学版）》2021年第5期1245-1251,共7页Journal of Jilin University:Science Edition

基　　金：贵州省科学技术基金(批准号:黔科合LH字[2016]7023号);教育部高等教育司产学合作协同育人项目(批准号:201801232042;201802341052)

摘　　要：为解决企业客户价值体现问题,提出一种TFA客户细分改进模型,以客户发展空间T、购买频次F和平均购买额A为指标,充分体现客户的价值和发展空间.首先,引入局部密度值ρ和信息熵H,改进K-means聚类算法,以优化传统K-means聚类方法初始聚类中心的选取问题;其次,通过搭建机器学习框架,对选取人工数据集及真实数据集进行聚类实验,验证模型的有效性.实验结果表明,该模型能有效分类客户,充分反映客户价值及其发展空间,并通过改进聚类算法提升了算法效率.In order to solve the problem of the reflection of corporate customer value,we proposed an improved model of TFA customer segmentation,which took customer development space T,purchase frequency F,and average purchase amount A as indicators to fully reflect the customer value and development space.Firstly,the K-means clustering algorithm was improved by introducing local density valueρand information entropy H to optimize the traditional K-means clustering method in the initial clustering center selection problem.Secondly,by building a machine learning framework,clustering experiments were carried out on selected artificial data sets and real data sets to verify the effectiveness of the model.The experimental results show that the model can more effectively classify customers,fully reflect the customer value and its development space,and improve the efficiency of the algorithm by improving the clustering algorithm.

关 键 词：客户分类 客户发展空间 K-MEANS算法 初始聚类中心 密度信息熵 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]