Effective data mining using neural networks

Hongjun Lu, Rudy Setiono, Huan Liu

Research output: Contribution to journalArticlepeer-review

304 Scopus citations


Classification is one of the data mining problems receiving great attention recently in the database community. This paper presents an approach to discover symbolic classification rules using neural networks. Neural networks have not been thought suited for data mining because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by humans. With the proposed approach, concise symbolic rules with high accuracy can be extracted from a neural network. The network is first trained to achieve the required accuracy rate. Redundant connections of the network are then removed by a network pruning algorithm. The activation values of the hidden units in the network are analyzed, and classification rules are generated using the result of this analysis. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of standard data mining test problems.

Original languageEnglish (US)
Pages (from-to)957-961
Number of pages5
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number6
StatePublished - 1996
Externally publishedYes


  • Classification
  • Data mining
  • Network pruning
  • Neural networks
  • Rule extraction

ASJC Scopus subject areas

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


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