Mining the customer behavior using web usage mining in e-commerce

Mahendra Pratap Yadav
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引用次数: 47

Abstract

With the explosive growth of information sources available on the WWW, it has become an important tool for users in order to find, extract, filter and evaluate the desired information and resources. The main purpose of this paper is to study the customer's behavior using the Web mining techniques and its application in e-commerce to mine customer behavior. The concept of Web mining describing the process of Web data mining in detail: source data collection, data pre-processing, pattern discovery, pattern analysis and cluster analysis. With the advanced information technologies, server are now able to collect and store mountains of data, describing their numerous contributions and different customer profiles, from which they seek to derive information about their customer's requirements. Conventional methods are no longer appropriate for these business situations to find the customer behavior. The principle of data mining is to cluster customer segments by using K-Means algorithm in which input data comes from web log of various e-commerce websites. Hence, determine the relationship between Web data mining and e-commerce and also to apply Web mining technology in e-commerce.
利用网络使用挖掘技术挖掘电子商务中的客户行为
随着万维网上可用信息源的爆炸式增长,它已成为用户查找、提取、过滤和评估所需信息和资源的重要工具。本文的主要目的是利用Web挖掘技术研究客户行为,并将其应用于电子商务中对客户行为进行挖掘。Web挖掘的概念详细描述了Web数据挖掘的过程:源数据采集、数据预处理、模式发现、模式分析和聚类分析。随着先进的信息技术的发展,服务器现在能够收集和存储大量的数据,这些数据描述了他们的大量贡献和不同的客户档案,从中他们寻求获得有关客户需求的信息。传统的方法不再适合这些业务情况来发现客户的行为。数据挖掘的原理是利用K-Means算法对客户群进行聚类,该算法的输入数据来源于各电子商务网站的web日志。因此,确定了Web数据挖掘与电子商务的关系,并将Web挖掘技术应用于电子商务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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