摘要
在大中型企业(特别是跨国企业)中会涉及到大量的客户,而不同的客户对产品的需求不一样,因此对客户进行分类对于大中型企业来说是非常有必要的,这关系到企业的产品营销战略、企业生存等问题。文章利用K-Means算法对客户进行分类,在实践应用中发现K-Means算法存在一些问题,这是K-Means算法固有的缺陷,文章提出了两个方面的改进,包括K值自适应确定和初始聚类中心的确定,这样省去了寻找最佳K值的麻烦,也减少了因随机确定聚类中心而导致的精度损失。改进后的算法应用到客户聚类实践中,验证了所设计算法的有效性,也证明了改进后的算法Chen-Means获得了100%的精确度,即改进后的算法具有明显的优势。
Large and medium-sized enterprises(especially multinational enterprises)will involve a large number of customers,and different customers have different needs for products.Therefore,it is very necessary for large and medium-sized enterprises to classify customers,which is related to enterprises.Product marketing strategy,business survival and other issues.This article uses the K-Means algorithm to classify customers.In practice,it is found that there are some problems with the K-Means algorithm.This is an inherent defect of the K-Means algorithm.This article proposes two improvements,including K-value adaptive determination and the determination of the initial cluster center saves the trouble of finding the best K value,and also reduces the accuracy loss caused by randomly determining the cluster center.The improved algorithm is applied to customer clustering practice,which verifies the effectiveness of the designed algorithm,and also proves that the improved algorithm Chen-Means has obtained 100%accuracy,that is,the improved algorithm has obvious advantages.
作者
陈新华
Chen Xinhua(Fujian Provincial Sports Management Center for Disabled Persons,Fujian 350011,China)
出处
《信息通信》
2020年第9期35-37,共3页
Information & Communications
