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Sensors 2011, 11(5), 4622-4647; doi:10.3390/s110504622
Article

A New Data Mining Scheme Using Artificial Neural Networks

1,*  and 2
Received: 11 February 2011 / Revised: 11 April 2011 / Accepted: 14 April 2011 / Published: 28 April 2011
(This article belongs to the Section Physical Sensors)
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Abstract

Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs) have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be explained. To enhance the explanation of ANNs, a novel algorithm to extract symbolic rules from ANNs has been proposed in this paper. ANN methods have not been effectively utilized for data mining tasks because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by human experts. With the proposed approach, concise symbolic rules with high accuracy, that are easily explainable, can be extracted from the trained ANNs. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the accuracy. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of benchmark data mining classification problems.
Keywords: data mining; neural networks; symbolic rules; weight freezing; constructive algorithm; pruning; clustering; rule extraction; symbolic rules data mining; neural networks; symbolic rules; weight freezing; constructive algorithm; pruning; clustering; rule extraction; symbolic rules
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Kamruzzaman, S.M.; Sarkar, A.M.J. A New Data Mining Scheme Using Artificial Neural Networks. Sensors 2011, 11, 4622-4647.

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