NeuroRule: A Connectionist Approach to Data Mining
This addresses the need for interpretable data mining in large databases, offering a connectionist alternative to symbolic methods, though it appears incremental in applying neural networks to a known bottleneck.
The paper tackles the problem of extracting human-interpretable classification rules from neural networks, which were previously considered unsuitable for data mining due to opaque knowledge representation. It presents algorithms that generate rules as concise as or more concise than symbolic methods, with experimental results and comparisons to prior work.
Classification, which involves finding rules that partition a given data set into disjoint groups, is one class of data mining problems. Approaches proposed so far for mining classification rules for large databases are mainly decision tree based symbolic learning methods. The connectionist approach based on neural networks has been thought not well suited for data mining. One of the major reasons cited is that knowledge generated by neural networks is not explicitly represented in the form of rules suitable for verification or interpretation by humans. This paper examines this issue. With our newly developed algorithms, rules which are similar to, or more concise than those generated by the symbolic methods can be extracted from the neural networks. The data mining process using neural networks with the emphasis on rule extraction is described. Experimental results and comparison with previously published works are presented.