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New Optimization Models For Data Mining

Author

Listed:
  • FRED W. GLOVER

    (University of Colorado, Campus Box 419, Boulder, Colorado 80309, USA)

  • GARY KOCHENBERGER

    (Decision Sciences, University of Colorado, School of Business, Denver, Colorado 80217-3364, USA)

Abstract

In recent years modern methods of optimization have contributed greatly to the advances in data mining and related areas. These contributions continue today and promise to further advance the state of the art both in terms of modeling innovations and new solution methodologies. In this paper, we present a new modeling and solution methodology for unsupervised clustering. Preliminary computational experience is given to illustrate the approach. This methodology is part of our current research and offers considerable opportunity for additional investigation to be conducted by other researchers.

Suggested Citation

  • Fred W. Glover & Gary Kochenberger, 2006. "New Optimization Models For Data Mining," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 605-609.
    • Handle: RePEc:wsi:ijitdm:v:05:y:2006:i:04:n:s0219622006002143
    DOI: 10.1142/S0219622006002143
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    Cited by:

    1. Si He & Nabil Belacel & Alan Chan & Habib Hamam & Yassine Bouslimani, 2016. "A Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Automatic Identification of Clusters," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(05), pages 949-974, September.
    2. Harun Pirim & Burak Eksioglu & Fred W. Glover, 2018. "A Novel Mixed Integer Linear Programming Model for Clustering Relational Networks," Journal of Optimization Theory and Applications, Springer, vol. 176(2), pages 492-508, February.

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