面向电子商务的WEB数据挖掘研究

发布时间：2017-12-31 16:09

本文关键词：面向电子商务的WEB数据挖掘研究　出处：《中南大学》2011年硕士论文　论文类型：学位论文

【摘要】：随着互联网的迅速发展,互联网上的信息量快速增长。大多数用户无法从庞大的互联网上获得自己想要的信息,很多电子商务网站也无法针对目标用户提供用户感兴趣的内容。当前,在电子商务领域,数据挖掘技术与Web技术正不断融合,利用Web挖掘技术,企业决策者能为用户提供更有效的资源,为企业获得更高的收益。 Web挖掘的研究已经建立了较好的理论体系,也取得了一些应用成果,但是,在针对Web数据的挖掘智能化与站点设计基于商业知识发现的总体规划方面存在较多的问题,还需继续研究。本文是针对这一方面进行一些尝试性的研究工作,主要工作内容如下：理解了数据挖掘、Web挖掘相关的基本理论和主要技术；研究了在电子商务领域Web数据挖掘的主要应用以及面临的关键问题；具体分析了在电子商务领域数据挖掘的主要内容和数据源；设计了面向电子商务Web数据挖掘总体模型。详细阐述了基于电子商务Web数据挖掘预处理过程；运用算法描述了数据清洗与净化、用户识别、会话识别和事务识别等过程；研究在电子商务领域运用关联规则技术进行挖掘模式发现,通过实例分析详细探讨了Apriori经典关联规则挖掘算法的具体运用；提出了Apriori算法的不足并对其进行了算法优化；探讨了基于用户浏览兴趣进行用户模糊聚类分析；研究了聚类常用方法,分析了模糊聚类的步骤,研究了用户浏览兴趣的度量及其方法,通过实例分析研究实现了基于用户访问频繁页面次数和耗时的模糊聚类过程。
[Abstract]:With the rapid development of the Internet, the amount of information on the Internet is growing rapidly. Most users can not get the information they want from the huge Internet. At present, in the field of electronic commerce, data mining technology and Web technology are constantly merging, using Web mining technology. Enterprise decision-makers can provide users with more effective resources and obtain higher profits for enterprises. The research of Web mining has established a good theoretical system, and has also made some application results, but. There are many problems in intelligent mining of Web data and overall planning of site design based on business knowledge discovery. This paper is to carry out some tentative research work in this field, the main work is as follows: The basic theory and main technology of data mining related to Web mining are understood. The main applications and key problems of Web data mining in the field of electronic commerce are studied. The main contents and data sources of data mining in the field of electronic commerce are analyzed in detail. A general model of Web data mining oriented to e-commerce is designed. The preprocessing process of data mining based on e-commerce Web is described in detail. The processes of data cleaning and purification, user identification, session identification and transaction identification are described by the algorithm. This paper studies the application of association rules technology to mining patterns in the field of electronic commerce, and discusses the application of Apriori classic association rules mining algorithm in detail through the analysis of examples. The deficiency of Apriori algorithm is put forward and the algorithm is optimized. The user fuzzy clustering analysis based on user's browsing interest is discussed. The common methods of clustering are studied, the steps of fuzzy clustering are analyzed, and the measurement and method of users' browsing interest are studied. The fuzzy clustering process based on the number of frequent page visits and the time consuming is realized through the case study.
【学位授予单位】：中南大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TP311.13

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