Using neural networks for data mining

This article describes neural-network learning algorithms that are able to produce comprehensible models, and that do not require excessive training times, and argues that these methods deserve a place in the tool boxes of data-mining specialists.

Abstract

Neural networks have been successfully applied in a wide range of supervised and unsupervised learning applications. Neural-network methods are not commonly used for data-mining tasks, however, because they often produce incomprehensible models and require long training times. In this article, we describe neural-network learning algorithms that are able to produce comprehensible models, and that do not require excessive training times. Specifically, we discuss two classes of approaches for data mining with neural networks. The first type of approach, often called rule extraction, involves extracting symbolic models from trained neural networks. The second approach is to directly learn simple, easy-to-understand networks. We argue that, given the current state-of-the-art, neural-network methods deserve a place in the tool boxes of data-mining specialists.