数据挖掘课程论文.doc

课程论文
〔 2014/ 2015 学年第一学期〕
课程名称
数据挖掘
论文名称
写作时间
2014
年
12
月
28
日
指导单位
计算机学院
指导教师
叶水仙
学生
班级学号
B12111823
学院(系)
管理学院
专业
信息管理与信息系统
数据挖掘在网络游戏客户关系管理的应用
摘要
数据挖掘技术在网络游戏客户关系管理中的应用现今,数据挖掘技术已经在各个领域得以应用,并有了相当的开展。许多学者对数据挖掘的研究及其在客户关系管理中的应用上获得了相当的突破,很多行业因此获益匪浅。在我国,网络游戏作为一个新兴不久的行业,拥有着巨大的市场和庞大的潜力,但随之而来的是愈发剧烈的市场竞争,一些主流的游戏运营商已经把目光投向了在传统行业中取得了巨大成功的客户关系管理体制。但是,不同于其他行业,网络游戏是建立在网络信息交流平台之上,它所搜集到的客户数据更为庞大和繁杂,那么怎样从如此庞大的数据中找到有效资料呢?这就需要应用到数据挖掘技术。本文简要介绍了网络游戏客户关系管理的模式和容以及几种主要的数据挖掘技术;着重利用决策树玩家进展了分类、利用神经网络对玩家流失进展了分析、利用粗糙集对玩家信息进展了挖掘。通过上述分析论证了数据挖掘技术在网络游戏客户关系管理中应用的可行性和有效性。
关键词:网络游戏;客户关系管理;数据挖掘;决策树;
Data mining in customer relationship work game applications
Abstract
Data mining technology in work game of today's customer relationship management, data mining techniques have been applied in various fields, and has considerable development. Many scholars study on data mining and gained considerable breakthrough in customer relationship management application, and therefore benefit many industries. In China, online games industry as an emerging soon, has a huge market and a huge potential, but the attendant is increasingly fierce market petition, some mainstream gaming operators have set their sights on traditional industries We have achieved great ess in customer relationship management system. However, unlike other industries, online games are built on top of work for information exchange platform, which it collect customer data more large and plex, then how to find effective information from such a large data in it" This requires the application of data mining techniques. This article briefly describes the mode and content of online games as well as customer relationship management several major data mining techniques; focus on the use of decision trees players were classified by using works for the loss of players were analyzed using rough set mining player information . Through the above analysis demonstrated the feasibility and effectiveness of data mining technology in