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Data-Driven Discovery of Quantitative Rules in Relational Databases
February 1993 (vol. 5 no. 1)
pp. 29-40

A quantitative rule is a rule associated with quantitative information which assesses the representativeness of the rule in the database. An efficient induction method is developed for learning quantitative rules in relational databases. With the assistance of knowledge about concept hierarchies, data relevance, and expected rule forms, attribute-oriented induction can be performed on the database, which integrates database operations with the learning process and provides a simple, efficient way of learning quantitative rules from large databases. The method involves the learning of both characteristic rules and classification rules. Quantitative information facilitates quantitative reasoning, incremental learning, and learning in the presence of noise. Moreover, learning qualitative rules can be treated as a special case of learning quantitative rules. It is shown that attribute-oriented induction provides an efficient and effective mechanism for learning various kinds of knowledge rules from relational databases.

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Index Terms:
data driven recovery; quantitative rules; relational databases; quantitative rule; quantitative information; concept hierarchies; data relevance; attribute-oriented induction; characteristic rules; classification rules; quantitative reasoning; incremental learning; knowledge rules; knowledge based systems; learning (artificial intelligence); relational databases
Citation:
J. Han, Y. Cai, N. Cercone, "Data-Driven Discovery of Quantitative Rules in Relational Databases," IEEE Transactions on Knowledge and Data Engineering, vol. 5, no. 1, pp. 29-40, Feb. 1993, doi:10.1109/69.204089
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