Please use this identifier to cite or link to this item: https://doi.org/10.1109/69.553163
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dc.titleEffective data mining using neural networks
dc.contributor.authorLu, H.
dc.contributor.authorSetiono, R.
dc.contributor.authorLiu, H.
dc.date.accessioned2014-10-27T06:02:10Z
dc.date.available2014-10-27T06:02:10Z
dc.date.issued1996
dc.identifier.citationLu, H., Setiono, R., Liu, H. (1996). Effective data mining using neural networks. IEEE Transactions on Knowledge and Data Engineering 8 (6) : 957-961. ScholarBank@NUS Repository. https://doi.org/10.1109/69.553163
dc.identifier.issn10414347
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/99253
dc.description.abstractClassification 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. ©1996 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/69.553163
dc.sourceScopus
dc.subjectClassification
dc.subjectData mining
dc.subjectNetwork pruning
dc.subjectNeural networks
dc.subjectRule extraction
dc.typeArticle
dc.contributor.departmentINFORMATION SYSTEMS & COMPUTER SCIENCE
dc.description.doi10.1109/69.553163
dc.description.sourcetitleIEEE Transactions on Knowledge and Data Engineering
dc.description.volume8
dc.description.issue6
dc.description.page957-961
dc.description.codenITKEE
dc.identifier.isiutA1996WC05700010
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