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Buying Customer Classification in Automotive Corporation with Decision Tree

의사결정트리를 통한 자동차산업의 구매패턴분류

  • 이병엽 (배재대학교 전자상거래학과) ;
  • 박용훈 (충북대학교 전기전자컴퓨터공학부) ;
  • 유재수 (충북대학교 전기전자컴퓨터공학부)
  • Published : 2010.02.28

Abstract

Generally, data mining is the process of analyzing data from different perspectives and summarizing it into useful information that can be used to increase revenue, cuts costs, or both. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Data mining is one of the fastest growing field in the computer industry. Because of According to computer technology has been improving, Massive customer data has stored in database. Using this massive data, decision maker can extract the useful information to make a valuable plan with data mining. Data mining offers service providers great opportunities to get closer to customer. Data mining doesn't always require the latest technology, but it does require a magic eye that looks beyond the obvious to find and use the hidden knowledge to drive marketing strategies. Automotive market face an explosion of data arising from customer but a rate of increasing customer is getting lower. therefore, we need to determine which customer are profitable clients whom you wish to hold. This paper builds model of customer loyalty detection and analyzes customer buying patterns in automotive market with data mining using decision tree as a quinlan C4.5 and basic statics methods.

Keywords

Data Mining;Classification;Decision Tree

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