NeuroRule: A Connectionist Approach to Data Mining

TL;DR

This paper introduces a neural network-based method for data mining that extracts human-readable rules, demonstrating comparable or more concise rules than traditional symbolic approaches.

Contribution

It presents new algorithms for extracting explicit, interpretable rules from neural networks, addressing a key limitation of connectionist models in data mining.

Findings

Rules comparable or more concise than symbolic methods

Effective rule extraction from neural networks demonstrated

Experimental results show competitive performance

Abstract

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.