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基于主成分分析的B2C客户分类方法研究

ON PCA-BASED CUSTOMER CLASSIFICATION METHOD FOR B2C
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摘要 随着电子商务的发展,以"客户为中心"已成为电子商务企业的经营策略,而任何高效的客户关系管理都是以扎实的客户分类为基础。然而电子商务中所搜集到的客户信息往往具有海量、高维度和不完备等特点,如何对其正确、高效地分类是一个难题。根据电子商务客户信息的特点,构建B2C客户分类模型,提出了先对客户信息进行主成分分析以消除属性之间的依赖性,而后用朴素贝叶斯算法进行分类的新方法。实验表明了该方法的有效性。 With the development of e-commerce,＂customer-eentred＂ has become the operating strategy of e-commerce companies,and any efficient customer relationship management is based on solid customer classification. However, customers＇ information collected in e-commerce is always huge,muhi-dimension and incomplete, how to classify the information properly and efficiently is a hard problem. According to the characteristics of e-connneree customers,a model of B2C customer elassification was established in the paper. A new method was put forward as well,in it the principal components analysis on customer information is performed first for removing dependence between the attributes,and then the customers are classified with ha？ ve Bayes algorithm. Experiments illustrate the efficiency of the method.
作者 周玉敏 邓维斌
机构地区 重庆邮电大学电子商务与现代物流实验室
出处 《计算机应用与软件》 CSCD 2009年第6期72-74,95,共4页 Computer Applications and Software
基金 重庆市自然科学基金重点项目(2008BA2017)
关键词 主成分分析 电子商务 B2C 客户分类 朴素贝叶斯 Principal components analysis （PCA） E-commerce Business to customer（B2C） Customer classification Naive Bayes
分类号 TP311 [自动化与计算机技术—计算机软件与理论] O212.4 [理学—概率论与数理统计]
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计算机应用与软件
2009年 第6期

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