K-means Clustering Algorithm Optimization And Its Application In E-commerce Platform Precision Marketing

With the booming development of e-commerce industry,e-commerce enterprises will face thousands of consumers every day.Acquiring the characteristics of customers and adopting different sales strategies for different categories of customers can not only effectively reduce operating costs,increase sales and increase profits,but also gain customer recognition and bond more customers.Precision marketing refers to selling the products required by the customers to the consumers in an appropriate way on the basis of different values and consumption concepts of each consumer at an appropriate time.Precision marketing can help enterprises achieve the goal of adopting corresponding marketing strategies according to the characteristics of customer groups.The key to precision marketing is to classify users,and the commonly used algorithm is k-means algorithm.The traditional K-means algorithm is widely used in data mining because of its simple principle and fast operation speed.However,for different data sets,the algorithm of similarity measurement and the selection of initial clustering center are limited.This paper improves the traditional K-means algorithm and proposes hC-Kmeans algorithm.The improvement of the traditional K-means algorithm mainly includes two aspects:one is the weighted processing of similarity measurement;the other is the optimization of the selection strategy of the initial clustering center.Firstly,the density values of each sample point are calculated according to the density formula,and the sample point with the largest density value is selected as the first clustering center.Then,the density method is combined with the maximum and minimum distance method to select other initial clustering centers.Then the sample points are grouped into each cluster according to the weighted distance,and finally the cluster center of each cluster is iteratively updated by the weighted distance until the cluster center no longer changes.Experiments show that the HC-Kmeans algorithm proposed in this paper is more accurate than the traditional K-means algorithm in clustering results.Finally,based on HC-Kmeans algorithm,the consumption behavior of consumers in Vipshop is analyzed,and the precise clustering of consumers is divided into four groups:high-end luxury consumers,high-end light luxury consumers,middle-end mass consumers,and low-end hard-demand consumers.Then it further analyzes the characteristics of each consumer group and proposes different precision marketing strategies for different consumer groups.