Knowledge Discovery in Databases: An Attribute-Oriented Approach.

Jiawei Han, Yandong Cai, Nick Cercone: Knowledge Discovery in Databases: An Attribute-Oriented Approach. VLDB 1992: 547-559

@inproceedings{DBLP:conf/vldb/HanCC92,
  author    = {Jiawei Han and
               Yandong Cai and
               Nick Cercone},
  editor    = {Li-Yan Yuan},
  title     = {Knowledge Discovery in Databases: An Attribute-Oriented Approach},
  booktitle = {18th International Conference on Very Large Data Bases, August
               23-27, 1992, Vancouver, Canada, Proceedings},
  publisher = {Morgan Kaufmann},
  year      = {1992},
  isbn      = {1-55860-151-1},
  pages     = {547-559},
  ee        = {db/conf/vldb/HanCC92.html},
  crossref  = {DBLP:conf/vldb/92},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Knowledge discovery in databases, or data mining, is an important issue in the development of data- and knowledge-base systems. An attribute-oriented induction method has been developed for knowledge discovery in databases. The method integrates a machine learning paradigm, especially learning-from-examples techniques, with set-oriented database operations and extracts generalized data from actual data in databases. An attribute-oriented concept tree ascension technique is applied in generalization, which substantially reduces the computational complexity of database learning processes. Different kinds of knowledge rules, including characteristic rules, discrimination rules, quantitative rules, and data evolution regularities can be discovered efficiently using the attribute-oriented approach. In addition to learning in relational databases, the approach can be applied toknowledge discovery in nested relational and deductive databases. Learning can also be performed with databases containing noisy data and exceptional cases using database statistics. Furthermore, the rules discovered can be used to query database knowledge, answer cooperative queries and facilitate semantic query optimization. Based upon these principles, a prototyped database learning system, DBLEARN, has been constructed for experimentation.

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Li-Yan Yuan (Ed.): 18th International Conference on Very Large Data Bases, August 23-27, 1992, Vancouver, Canada, Proceedings. Morgan Kaufmann 1992, ISBN 1-55860-151-1
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References

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