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Pattern-Based Discriminants in the Logical Analysis of Data

In: Data Mining in Biomedicine

Author

Listed:
  • Sorin Alexe

    (RUTCOR - Rutgers University Center for Operations Research)

  • Peter L. Hammer

    (RUTCOR - Rutgers University Center for Operations Research)

Abstract

Based on the concept of patterns, fundamental for the Logical Analysis of Data (LAD), we define a numerical score associated to every observation in a dataset, and show that its use allows the classification of most of the observations left unclassified by LAD. The accuracy of this extended LAD classification is compared on several publicly available benchmark datasets to that of the original LAD classification, and to that of the classifications provided by the most frequently used statistical and data mining methods.

Suggested Citation

  • Sorin Alexe & Peter L. Hammer, 2007. "Pattern-Based Discriminants in the Logical Analysis of Data," Springer Optimization and Its Applications, in: Panos M. Pardalos & Vladimir L. Boginski & Alkis Vazacopoulos (ed.), Data Mining in Biomedicine, pages 3-23, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-69319-4_1
    DOI: 10.1007/978-0-387-69319-4_1
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    Cited by:

    1. Bagchi, Prabir & Lejeune, Miguel A. & Alam, A., 2014. "How supply competency affects FDI decisions: Some insights," International Journal of Production Economics, Elsevier, vol. 147(PB), pages 239-251.
    2. Chun-An Chou & Tibérius O. Bonates & Chungmok Lee & Wanpracha Art Chaovalitwongse, 2017. "Multi-pattern generation framework for logical analysis of data," Annals of Operations Research, Springer, vol. 249(1), pages 329-349, February.

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