基于昆明市工商管理数据的数据挖掘与分析

发布时间：2018-03-24 19:51

本文选题：工商管理系统　切入点：数据挖掘　出处：《云南大学》2016年硕士论文

【摘要】：随着大数据时代的到来,数据的利用价值日益受到各行业的重视。数据挖掘在对已有的数据进行二次利用方面显示出了优越性,这能够对我们的生活产生巨大的经济效益。工商管理部门为提高管理工作效率,适应现代化的管理,已经逐渐建立了自己的业务管理系统,实现了经济户口的数字化。但其当前的业务系统只能提供日常的事务管理,且随着数据的爆发式增长,数据库变得非常庞大,业务系统无法提供深层次的数据统计分析与挖掘,想要从业务系统中获取有用的信息变得越来越困难,严重影响了对数据的充分利用。因此,客观现实迫切要求在工商管理系统中采用数据挖掘技术,对工商管理数据进行深入了解和分析。本文基于昆明市工商行政管理局的管理数据,运用数据挖掘相关技术,详细说明了在工商管理数据中实际运用数据挖掘技术的方法,给出了基于分类分析方法的工商管理数据挖掘模型。同时,以Java语言口Spring.Struts.Hibernate三大开源框架为开发工具,设计并实现了工商管理数据挖掘系统,获得了一些在市场主体发展趋势上的应用实例。此外,通过分析挖掘需求、现有数据特点以及决策树算法,本文对C4.5决策树算法做出了一些修改和完善。与未改进的算法相比较,改进后的算法采用属性标准优化,引入用户关注度,使得挖掘结果更能满足实际需求。
[Abstract]:With the arrival of big data era, the value of data utilization has been paid more and more attention by various industries. Data mining has shown its superiority in reusing existing data. In order to improve the efficiency of management and adapt to modern management, the industrial and commercial administration has gradually established its own business management system. But the current business system can only provide daily transaction management, and with the explosive growth of data, the database becomes very large, the business system can not provide the deep level of statistical analysis and mining data. It is becoming more and more difficult to obtain useful information from business systems, which seriously affects the full utilization of data. Based on the management data of Kunming Municipal Administration for Industry and Commerce, this paper explains in detail the practical application of data mining technology in industrial and commercial management data by using the relevant data mining technology. In this paper, a business administration data mining model based on classification analysis method is presented. At the same time, the business administration data mining system is designed and implemented with the three open source frameworks of Java language port Spring.Struts.Hibernate as the development tool. In addition, through the analysis of mining requirements, the characteristics of existing data and the decision tree algorithm, In this paper, the C4.5 decision tree algorithm is modified and improved. Compared with the unimproved algorithm, the improved algorithm adopts the attribute standard optimization, and introduces user concern, which makes the mining results more satisfied with the actual demand.
【学位授予单位】：云南大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP311.13

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