关联规则挖掘在天猫商城中的应用研究

发布时间：2018-04-23 21:34

本文选题：电子商务 + 关联规则挖掘　；参考：《五邑大学》2013年硕士论文

【摘要】：随着Internet技术的不断发展,电子商务这一现代商业模式以其高效率、低成本和不受时空限制的特点成为企业商务活动发展的趋势。而天猫商城是亚洲最大的电子商务交易平台,其拥有近5亿的注册用户数,每天有超过6000万的固定访客,同时每天的在线商品数已经超过了8亿件,是中国电子商务网站发展的奇迹。巨大数量的用户创造了巨大的交易数据,然而从这些繁杂的交易数据中我们能得到有价值的信息却相对较少,如何能从这些交易数据中获取有利于卖家商业运作以及制定营销策略的信息成为不容忽略的重要问题。关联规则挖掘(Association Rules Mining)是通过分析每个数据,从大量数据中寻找规律的技术。该技术的出现为电子商务行为提供了强大的数据分析支持,将关联规则挖掘技术应用到大量的、复杂的交易数据中去,才能体现出关联规则挖掘技术的研究价值,毫无疑问电子商务是数据挖掘应用的最佳对象。通过对交易数据的挖掘,如商品的交易数量,交易种类,交易时间等,提取相关的交易知识,将复杂,无序的交易数据,变成卖家分析市场,制定经营策略,管理客户关系的有力依据,从而实现电子商务活动的真正价值。论文讨论了关联规则挖掘中的主要算法之一,Apriori算法,分析了该算法在挖掘大量交易数据中的具体实现过程。通过记录大量的天猫商城交易数据,建立交易数据事务数据库,分别从买家和商品两个角度进行讨论,对实际数据进行算法应用分析,结合实例证明了该算法在电子商务数据应用关联规则挖掘中的有效性,并根据算法挖掘得到的关联规则,结合微博营销的方式,最终达到提升销售的效果。
[Abstract]:With the development of Internet technology, E-commerce, as a modern business model, has become the trend of the development of business activities because of its high efficiency, low cost and no limitation of time and space. Tmall Mall is the largest e-commerce trading platform in Asia, with nearly 500 million registered users, more than 60 million regular visitors a day, and more than 800 million online goods every day. It is the miracle of the development of Chinese e-commerce website. A huge number of users create huge amounts of transaction data, but we get relatively little valuable information from these complex trading data. How to obtain the information that is beneficial to the seller's business operation and make the marketing strategy from these transaction data has become an important problem that can not be ignored. Association Rules Mining is a technique to find rules from a large number of data by analyzing each data. The emergence of this technology provides a powerful data analysis support for e-business behavior. Only when association rules mining technology is applied to a large number of complex transaction data, can the research value of association rule mining technology be reflected. There is no doubt that electronic commerce is the best object for data mining applications. Through the mining of transaction data, such as the quantity, type and time of trade, and so on, the relevant transaction knowledge is extracted, and the complicated and disordered transaction data is turned into the seller to analyze the market and formulate the management strategy. Management of customer relationships on the basis of strength, so as to achieve the real value of e-commerce activities. In this paper, the Apriori algorithm, one of the main algorithms in association rule mining, is discussed, and the implementation process of the algorithm in mining a large number of transaction data is analyzed. By recording a large number of trading data of Tmall Mall, establishing transaction data transaction database, discussing the transaction data from two angles of buyer and commodity, analyzing the algorithm application of the actual data. The validity of the algorithm in the application of association rules in electronic commerce data is proved by an example. According to the association rules obtained by the algorithm and the marketing method of Weibo, the effect of sales promotion is finally achieved.
【学位授予单位】：五邑大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP311.13;F724.6

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