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Knowledge discovery from structural data

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Abstract

Discovering repetitive substructure in a structural database improves the ability to interpret and compress the data. This paper describes the Subdue system that uses domain-independent and domain-dependent heuristics to find interesting and repetitive structures in structural data. This substructure discovery technique can be used to discover fuzzy concepts, compress the data description, and formulate hierarchical substructure definitions. Examples from the domains of scene analysis, chemical compound analysis, computer-aided design, and program analysis demonstrate the benefits of the discovery technique.

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Authors and Affiliations

  1. Department of Computer Science Engineering, University of Texas at Arlington, USA

    Diane J. Cook, Lawrence B. Holder & Surnjani Djoko

Authors
  1. Diane J. Cook
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  2. Lawrence B. Holder
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  3. Surnjani Djoko
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Supported by NASA grant NAS5-32337.

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Cook, D.J., Holder, L.B. & Djoko, S. Knowledge discovery from structural data. J Intell Inf Syst 5, 229–248 (1995). https://doi.org/10.1007/BF00962235

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