ACM SIGMOD Anthology TKDE dblp.uni-trier.de

Data-Driven Discovery of Quantitative Rules in Relational Databases.

Jiawei Han, Yandong Cai, Nick Cercone: Data-Driven Discovery of Quantitative Rules in Relational Databases. IEEE Trans. Knowl. Data Eng. 5(1): 29-40(1993)
@article{DBLP:journals/tkde/HanCC93,
  author    = {Jiawei Han and
               Yandong Cai and
               Nick Cercone},
  title     = {Data-Driven Discovery of Quantitative Rules in Relational Databases},
  journal   = {IEEE Trans. Knowl. Data Eng.},
  volume    = {5},
  number    = {1},
  year      = {1993},
  pages     = {29-40},
  ee        = {db/journals/tkde/HanCC93.html},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

A quantitative rule is a rule associated with quantitative information which assesses the representativeness of the rule in the database. In this paper, we develop an efficient induction method 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. Our method learns 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. Our paper shows that attribute-oriented induction provides an efficient and effective mechanism for learning various kinds of knowledge rules from relational databases.

Copyright © 1993 by The Institute of Electrical and Electronic Engineers, Inc. (IEEE). Abstract used with permission.

Joint ACM SIGMOD / IEEE Computer Society Anthology

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