Abstract

Data mining is automated or semi-automated Knowledge Discovery from large amounts of stored data in order to discovering meaningful patterns and rules. Marketing related data mining applied to market segmentation, customer services, credit and behavior scoring, and benchmarking. There are different classifiers including decision tree, ID3, CART, Quest, Neural networks, Association, Bayesian, and etc. In this study, ten classifiers are examined to identify important issues in mining marketing data. Classification accuracy, learning speed, Classification speed, Missing value, and robustness are some indices included to compare and contrast the classifiers. Shopping malls' consumer behavior data were used in our investigation. Results indicate that classifiers perform differently under different consumer data types.

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