Data-Driven Discovery of Quantitative Rules in Relational Databases

Jiawei Han, Yandong Cai, Nick Cercone

Research output: Contribution to journalArticlepeer-review


A quantitative rule is a rule associated with quantitative information which assesses the representativeness of the rule in the database. In this paper, 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. Our method learns both characteristic rules and classification rules. Quantitative information facilitates quantitative reasoning, incremental learning, and learning in the presence of noise. More-over, 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.

Original languageEnglish (US)
Pages (from-to)29-40
Number of pages12
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number1
StatePublished - Feb 1993
Externally publishedYes


  • Knowledge discovery in databases
  • attribute-oriented induction
  • characteristic rules
  • classification rules
  • data-driven learning algorithms
  • machine learning
  • quantitative rules

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


Dive into the research topics of 'Data-Driven Discovery of Quantitative Rules in Relational Databases'. Together they form a unique fingerprint.

Cite this