基于数据挖掘的校园超市销售智能分析与应用系统

发布时间：2018-03-26 22:16

本文选题：校园超市数据挖掘　切入点：OLAP　出处：《浙江工业大学》2016年硕士论文

【摘要】：随着互联网技术的快速发展,互联网信息呈现爆发式的增长,数据挖掘技术正是在这一背景下发展起来的一门新兴网络技术,打破了传统的数据分析规则,可以从海量数据中快速的挖掘出各种潜在信息,其有效的提高了当前互联网信息利用效率,对未来互联网技术进一步发展具有非常重要的意义。校园超市作为校园零售的主体,随着电子商务的快速发展,给校园超市经营带来了巨大的竞争和压力,如何深入了解校园超市零售商品之间的特点,发掘其潜在的有价值的信息,为客户提供更加优质的服务,是未来校园超市面临的一个重要的问题,因此,研究数据挖掘分析技术,并将其应用到校园超市销售数据分析中,具有一定的理论意义和实用价值。本文的主要工作如下:1)研究和分析了当前数据挖掘技术发展现状以及在超市销售数据分析中的应用现状,在此基础上对数据仓和数据挖掘相关技术进行了研究和分析,详细分析了联机分析处理(简称OLAP)和Apriori算法技术,结合实例分析对Apriori算法存在的问题进行了深入的研究,提出了相应的改进Apriori算法的设计,并通过实验分析验证了改进算法的正确性和可行性;2)结合校园超市销售数据的特点,深入的研究和分析了OLAP和Apriori算法的特点,针对Apriori算法的在时间和空间上的消耗较大的原因,提出了基于OLAP和Apriori算法的混合挖掘模型的设计,通过该模型有效的提高数据挖掘分析效率,降低数据挖掘分析成本。3)采用先进的数据挖掘分析平台——weka,完成了基于OLAP和改进的Apriori算法的挖掘模型的实现,并对某校园超市的销售数据进行了分析,得出了相关商品之间的关联性,验证了本文数据挖掘模型设计的正确性和可靠性。
[Abstract]:With the rapid development of Internet technology and the explosive growth of Internet information, data mining technology is a new network technology developed under this background, which breaks the traditional data analysis rules. It can quickly excavate all kinds of potential information from massive data, which effectively improves the efficiency of Internet information utilization, and has very important significance for the further development of Internet technology. Campus supermarket is the main body of campus retail. With the rapid development of electronic commerce, it brings enormous competition and pressure to the management of campus supermarket. How to deeply understand the characteristics of the retail products of campus supermarket, explore its potential valuable information, and provide better service for customers, It is an important problem that the campus supermarket will face in the future. Therefore, we study the technology of data mining and analysis, and apply it to the analysis of the sales data of campus supermarket. The main work of this paper is as follows: 1) Research and analysis of the current development of data mining technology and its application in supermarket sales data analysis. On this basis, the related technologies of data warehouse and data mining are studied and analyzed, and the on-line analytical processing (OLAP) and Apriori algorithm technology are analyzed in detail. The design of the corresponding improved Apriori algorithm is put forward, and the correctness and feasibility of the improved algorithm are verified by the experimental analysis. (2) combined with the characteristics of the campus supermarket sales data, the characteristics of the OLAP and Apriori algorithms are deeply studied and analyzed. In view of the large consumption of time and space in Apriori algorithm, a hybrid mining model based on OLAP and Apriori algorithm is proposed, which can effectively improve the efficiency of data mining and analysis. To reduce the cost of data mining analysis. 3) the mining model based on OLAP and improved Apriori algorithm is implemented by using the advanced data mining analysis platform, Weka. and the sales data of a campus supermarket are analyzed. The correlation between related commodities is obtained, and the correctness and reliability of the design of data mining model are verified.
【学位授予单位】：浙江工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP311.13

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