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Knowledge Discovery in Molecular Databases
December 1993 (vol. 5 no. 6)
pp. 985-987

An approach to knowledge discovery in complex molecular databases is described. The machine learning paradigm used is structured concept formation, in which object's described in terms of components and their interrelationships are clustered and organized in a knowledge base. Symbolic images are used to represent classes of structured objects. A discovered molecular knowledge base is successfully used in the interpretation of a high resolution electron density map.

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Index Terms:
case-based reasoning; chemical information retrieval; conceptual clustering; description logics; indexing; relational models; scene analysis; spatial concepts; spatial reasoning; structured concept formation; knowledge discovery; molecular databases; machine learning paradigm; knowledge base; symbolic images; molecular knowledge base; high resolution electron density map; case-based reasoning; chemistry computing; deductive databases; factographic databases; learning (artificial intelligence); relational databases; visual databases
D. Conklin, S. Fortier, J. Glasgow, "Knowledge Discovery in Molecular Databases," IEEE Transactions on Knowledge and Data Engineering, vol. 5, no. 6, pp. 985-987, Dec. 1993, doi:10.1109/69.250082
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