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Clustering for Data Privacy and Classification Tasks

In: Operations Research Proceedings 2013

Author

Listed:
  • Klaus B. Schebesch

    (“Vasile Goldiş” Western University of Arad
    “Vasile Goldiş” Western University of Arad)

  • Ralf Stecking

    (Carl von Ossietzky University of Oldenburg)

Abstract

Predictive classification is a part of data mining and of many related data-intensive research activities. In applications deriving from business intelligence, potentially valuable data from large databases often cannot be used in an unrestricted way. Privacy constraints may not allow the data modeler to use all of the existing feature variables in building the classification models. In certain situations, pre-processing the original data can lead to intermediate datasets, which hide private or commercially sensitive information but still contain information useful enough for building competitive classification models. To this end, we propose to cooperatively use both unsupervised Clustering and supervised Support Vector Machines. For an instance of real-life credit client scoring, we then evaluate our approach against the case of unrestricted use of all data features.

Suggested Citation

  • Klaus B. Schebesch & Ralf Stecking, 2014. "Clustering for Data Privacy and Classification Tasks," Operations Research Proceedings, in: Dennis Huisman & Ilse Louwerse & Albert P.M. Wagelmans (ed.), Operations Research Proceedings 2013, edition 127, pages 397-403, Springer.
    • Handle: RePEc:spr:oprchp:978-3-319-07001-8_54
    DOI: 10.1007/978-3-319-07001-8_54
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