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Understanding Credit Card User's Behaviour: A Data Mining Approach
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Author(s): A. de Carvalho (University of Guelph, Canada), A. P. Braga (Federal University of Minas Gerais, Brazil), S. O. Rezende (University of Sao Paulo, USA), E. Martineli (University of Sao Paulo, USA)and T. Ludermir (Federal University of Pernambuco, Brazil)
Copyright: 2002
Pages: 22
Source title:
Heuristic and Optimization for Knowledge Discovery
Source Author(s)/Editor(s): Hussein A. Abbass (University of New South Wales, Australia), Charles S. Newton (University of New South Wales, Australia)and Ruhul Sarker (University of New South Wales, Australia)
DOI: 10.4018/978-1-930708-26-6.ch013
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Abstract
In the last few years, a large number of companies are starting to realize the value of their databases. These databases, which usually cover transactions performed over several years, may lead to a better understanding of the customer’s profile, thus supporting the offer of new products or services. The treatment of these large databases surpasses the human ability to understand and efficiently deal with these data, creating the need for a new generation of tools and techniques to perform automatic and intelligent analyses of large databases. The extraction of useful knowledge from large databases is named knowledge discovery. Knowledge discovery is a very demanding task and requires the use of sophisticated techniques. The recent advances in hardware and software make possible the development of new computing tools to support such tasks. Knowledge discovery in databases comprises a sequence of stages. One of its main stages, the data mining process, provides efficient methods and tools to extract meaningful information from large databases. In this chapter, data mining methods are used to predict the behavior of credit card users. These methods are employed to extract meaningful knowledge from a credit card database using machine learning techniques. The performance of these techniques are compared by analyzing both their correct classification rates and the knowledge extracted in a linguistic representation (rule sets or decision trees). The use of a linguistic representation for expressing knowledge acquired by learning systems aims to improve the user understanding. Under this assumption, and to make sure that these systems will be accepted, several techniques have been developed by the artificial intelligence community, using both the symbolic and the connectionist approaches.
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