1. ¹K.U. Leuven, Leuven, Belgium
2. ²University of Southampton, Southampton, UK
3. ³University College Ghent, Ghent, Belgium

Correspondence: B Baesens, Department of Decision Sciences and Management, K.U. Leuven, Naamsestraat 69, B-3000 Leuven, Belgium. E-mail: Bart.Baesens@econ.kuleuven.be

Received June 2008; Accepted August 2008; Published online 11 February 2009.

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.