Predictive Adaptive Resonance Theory and Knowledge Discovery in Databases

  • Ah-Hwee Tan
  • Hui-Shin Vivien Soon
Conference paper

DOI: 10.1007/3-540-45571-X_21

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1805)
Cite this paper as:
Tan AH., Soon HS.V. (2000) Predictive Adaptive Resonance Theory and Knowledge Discovery in Databases. In: Terano T., Liu H., Chen A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science, vol 1805. Springer, Berlin, Heidelberg

Abstract

This paper investigates the scalability of predictive Adaptive Resonance Theory (ART) networks for knowledge discovery in very large databases. Although predictive ART performs fast and incremental learning, the number of recognition categories or rules that it creates during learning may become substantially large and cause the learning speed to slow down. To tackle this problem, we introduce an on-line algorithm for evaluating and pruning categories during learning. Benchmark experiments on a large scale data set show that on-line pruning has been effective in reducing the number of the recognition categories and the time for convergence. Interestingly, the pruned networks also produce better predictive performance.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Ah-Hwee Tan
    • 1
  • Hui-Shin Vivien Soon
    • 2
  1. 1.Kent Ridge Digital LabsSingapore
  2. 2.Ngee Ann PolytechnicSingapore

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