Attribute-oriented approach for learning classification rules from relational databases

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A classification rule is a rule which characterizes the properties that distinguish one class from other classes. An attribute-oriented induction algorithm which extracts classification rules from relational databases is developed. The algorithm adopts the artificial intelligence 'learning from examples' paradigm and applies an attribute-oriented concept tree ascending technique in the learning process. The technique integrates database operations with the learning process and provides a simple and efficient way of learning from large databases. The algorithm learns both conjunctive rules and restricted forms of disjunctive rules. Using database statistics, learning can be performed on databases containing noisy data and exceptions. An analysis and comparison with other algorithms show that attribute-oriented induction substantially reduces the complexity of database learning processes.

Original languageEnglish (US)
Title of host publicationProceedings - Sixth International Conference on Data Engineering
PublisherPubl by IEEE
Pages281-288
Number of pages8
ISBN (Print)0818620250
StatePublished - 1990
Externally publishedYes
EventProceedings - Sixth International Conference on Data Engineering - Los Angeles, CA, USA
Duration: Feb 5 1990Feb 9 1990

Other

OtherProceedings - Sixth International Conference on Data Engineering
CityLos Angeles, CA, USA
Period2/5/902/9/90

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Cai, Y. D., Cercone, N., & Han, J. (1990). Attribute-oriented approach for learning classification rules from relational databases. In Proceedings - Sixth International Conference on Data Engineering (pp. 281-288). Publ by IEEE.