文献类型：期刊文献

中文题名：基于PSO的K-means改进算法在证券客户细分中的应用

英文题名：Application of a Modified K-means Clustering Algorithm Based on PSO in Customer Segmentation of Securities Industry

作者：李英[1];吴圆圆[1];宁福锦[1]

机构：[1]华东理工大学商学院,上海200237

年份：2010

期号：7

起止页码：88

中文期刊名：现代图书情报技术

外文期刊名：New Technology of Library and Information Service

收录：北大核心:【北大核心2008】；CSSCI:【CSSCI2010_2011】；

语种：中文

中文关键词：粒子群优化;K—means算法;客户细分

外文关键词：PSO K- means algorithm Customer segmentation

摘要：针对K-means的缺陷,运用SD和PSO算法提出一种改进聚类算法,并通过Java编程实现。以上海某证券公司一个营业部的客户交易数据为例,将数据库中的数据分析、变换和标准化成适合挖掘的形式,将结合的聚类算法应用于细分模型进行聚类,并对聚类结果进行评价和分析。结果表明,利用改进的聚类算法能够得到更高质量的聚类结果。
According to the deficiencies of K - means clustering, the paper proposes a modified clustering algorithm which uses SD and PSO algorithm, and achieves this integrated algorithm in Java. In the analysis, the authors take eustomer transaction data of a securities company in Shanghai as an example. By transforming the database into a form suitable for mining, the paper applies the modified clustering algorithm to cluster segmentation model, and the clustering results show that the improved clustering algorithm can get higher quality clustering results.