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Analysis of credit card data based on data mining technique.

机译:基于数据挖掘技术的信用卡数据分析。

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摘要

In recent years, large amounts of data have accumulated with the application of database systems. Meanwhile, the requirements of applications have not been confined in the simple operations, such as search and retrieval, because these operations were not helpful in finding the valuable information from the databases. The hidden knowledge is hard to be handled by the present database techniques, so a great wealth of knowledge concealed in the databases is not developed and utilized mostly.;The paper concentrated on the research about data information in credit card by DM theories, techniques and methods to mine the valuable knowledge from the card. Firstly, the basic theories, key algorithms of DM techniques were introduced. The emphases were focused on the decision tree algorithms, neural networks, K-means algorithm in cluster and Apriori algorithm in association rule by understanding the background of bank and analyzing the knowledge available in the credit card. A preliminary analysis of credit card information, Industry and Business Bank at Tianjin Department, was performed based on the conversion and integration of data warehouse. The combined databases including information of customers and consumptive properties were established in accordance with the idea of data-warehouse. The data were clustered by K-means algorithm to find valuable knowledge and frequent intervals of transaction in credit card. Back propagation neural networks were designed to classify the information of credit card, which played an important role in evaluation and prediction of customers. In addition, the Apriori algorithm was achieved to process the abovementioned data, which could establish the relations between credit information of customers and consumption properties, and to find the association rule among credit items themselves, providing a solid foundation for further revision of information evaluation.;Our work showed that DM technique made great significance in analyzing the information of credit card, and laid down a firm foundation for further research in the retrieval information from the credit card.;Keywords: Database; Data; Credit card;Data mining aimed at finding the essential significant knowledge by automatic process of database. DM technique was one of the most challenging studies in database and decision-making fields. The data range processed was considerably vast from natural science, social science, business information to the data produced from scientific process and satellite observation. The present focuses of DM were changed from theories to practical application. Where the database existed, there were many projects about DM to be studied on.
机译:近年来,随着数据库系统的应用积累了大量的数据。同时,应用程序的需求还不局限于简单的操作,例如搜索和检索,因为这些操作无助于从数据库中找到有价值的信息。现有的数据库技术难以处理这些隐性知识,因此隐藏在数据库中的大量知识并未得到充分的开发和利用。论文主要通过DM理论,技术和方法对信用卡数据信息进行了研究。从卡中挖掘有价值的知识的方法。首先介绍了DM技术的基本理论,关键算法。通过理解银行的背景并分析信用卡中的可用知识,重点集中在决策树算法,神经网络,聚类中的K-means算法和关联规则中的Apriori算法。基于数据仓库的转换和集成,对天津市工商银行的信用卡信息进行了初步分析。根据数据仓库的概念,建立了包括客户信息和消费属性的组合数据库。通过K-means算法对数据进行聚类,以找到有价值的知识和信用卡交易的频繁间隔。设计了反向传播神经网络对信用卡信息进行分类,这在顾客评价和预测中起着重要的作用。另外,实现了Apriori算法来处理上述数据,可以建立顾客的信用信息与消费属性之间的关系,并找到信用项目自身之间的关联规则,为进一步修改信息评价提供了坚实的基础。 ;工作表明,DM技术对分析信用卡信息具有重要意义,为进一步研究信用卡信息打下了坚实的基础。数据;信用卡;数据挖掘,旨在通过数据库的自动处理来查找必要的重要知识。 DM技术是数据库和决策领域最具挑战性的研究之一。从自然科学,社会科学,商业信息到科学过程和卫星观测产生的数据,处理的数据范围非常广泛。 DM的当前焦点已从理论变为实际应用。在存在数据库的地方,有许多关于DM的项目需要研究。

著录项

  • 作者

    Zheng, Ying.;

  • 作者单位

    Universite du Quebec a Chicoutimi (Canada).;

  • 授予单位 Universite du Quebec a Chicoutimi (Canada).;
  • 学科 Computer Science.
  • 学位 M.Sc.
  • 年度 2009
  • 页码 83 p.
  • 总页数 83
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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