50 years of data mining and OR: upcoming trends and challenges

B Baesens (), C Mues (), D Martens and J Vanthienen
Additional contact information
B Baesens: K.U. Leuven
C Mues: University of Southampton
D Martens: K.U. Leuven
J Vanthienen: K.U. Leuven

Journal of the Operational Research Society, 2009, vol. 60, issue 1, S16-S23

Abstract: Abstract Data mining involves extracting interesting patterns from data and can be found at the heart of operational research (OR), as its aim is to create and enhance decision support systems. Even in the early days, some data mining approaches relied on traditional OR methods such as linear programming and forecasting, and modern data mining methods are based on a wide variety of OR methods including linear and quadratic optimization, genetic algorithms and concepts based on artificial ant colonies. The use of data mining has rapidly become widespread, with applications in domains ranging from credit risk, marketing, and fraud detection to counter-terrorism. In all of these, data mining is increasingly playing a key role in decision making. Nonetheless, many challenges still need to be tackled, ranging from data quality issues to the problem of how to include domain experts' knowledge, or how to monitor model performance. In this paper, we outline a series of upcoming trends and challenges for data mining and its role within OR.

Keywords: data mining; learning algorithms; decision support systems; applications; prediction (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (13)

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DOI: 10.1057/jors.2008.171

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