Effective data mining using neural networks

Classification is one of the data mining problems receiving great attention recently in the database community. The 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...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 1996-12, Vol.8 (6), p.957-961
Main Authors: Hongjun Lu, Setiono, R., Huan Liu
Format: Article
Language:English
Subjects:
Classification tree analysis
Data mining
Decision trees
Error analysis
Humans
Intelligent networks
Machine learning
Neural networks
Testing
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Summary:Classification is one of the data mining problems receiving great attention recently in the database community. The 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.
ISSN:1041-4347
1558-2191
DOI:10.1109/69.553163