Advanced search
1 file | 179.41 KB

50 years of Data Mining and OR: upcoming trends and challenges

Author
Organization
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, RULE EXTRACTION, CLASSIFICATION, OPTIMIZATION, ALGORITHMS, SEARCH, MODELS

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 179.41 KB

Citation

Please use this url to cite or link to this publication:

Chicago
Baesens, Bart, Christophe Mues, David Martens, and Jan Vanthienen. 2009. “50 Years of Data Mining and OR: Upcoming Trends and Challenges.” Journal of the Operational Research Society 60 (suppl. 1): S16–S23.
APA
Baesens, B., Mues, C., Martens, D., & Vanthienen, J. (2009). 50 years of Data Mining and OR: upcoming trends and challenges. JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 60(suppl. 1), S16–S23.
Vancouver
1.
Baesens B, Mues C, Martens D, Vanthienen J. 50 years of Data Mining and OR: upcoming trends and challenges. JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY. 2009;60(suppl. 1):S16–S23.
MLA
Baesens, Bart et al. “50 Years of Data Mining and OR: Upcoming Trends and Challenges.” JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 60.suppl. 1 (2009): S16–S23. Print.
@article{747863,
  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.},
  author       = {Baesens, Bart and Mues, Christophe and Martens, David and Vanthienen, Jan},
  issn         = {0160-5682},
  journal      = {JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY},
  keywords     = {data mining,learning algorithms,decision support systems,applications,prediction,RULE EXTRACTION,CLASSIFICATION,OPTIMIZATION,ALGORITHMS,SEARCH,MODELS},
  language     = {eng},
  number       = {suppl. 1},
  pages        = {S16--S23},
  title        = {50 years of Data Mining and OR: upcoming trends and challenges},
  url          = {http://dx.doi.org/10.1057/jors.2008.171},
  volume       = {60},
  year         = {2009},
}

Altmetric
View in Altmetric
Web of Science
Times cited: