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A Pruning Approach To Pattern Discovery

Author

Listed:
  • HSIAO-FAN WANG

    (Department of Industrial Engineering and Engineering Management, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu 300, Taiwan, ROC)

  • ZU-WEN CHAN

    (Department of Industrial Engineering and Engineering Management, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu 300, Taiwan, ROC)

Abstract

In this study, we proposed a general pruning procedure to reduce the dimension of a large database so that the properties of the extracted subset can be well defined. Since learning functions have been widely applied, we take this group of functions as an example to demonstrate the proposed procedure. Based on the concept of Support Vector Machine (SVM), three major stages of preliminary pruning, fitting function, and refining are proposed to discover a subset that possess the characteristics of some learning function from the given large data set. Three models were used to illustrate and evaluate the proposed pruning procedure and the results have shown to be promising in application.

Suggested Citation

  • Hsiao-Fan Wang & Zu-Wen Chan, 2008. "A Pruning Approach To Pattern Discovery," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(04), pages 721-736.
    • Handle: RePEc:wsi:ijitdm:v:07:y:2008:i:04:n:s0219622008003186
    DOI: 10.1142/S0219622008003186
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