Comparison of relational methods and attribute-based methods for data mining in intelligent systems

Abstract

Most of the data mining methods in real-world intelligent systems are attribute-based machine learning methods such as neural networks, nearest neighbors and decision trees. They are relatively simple, efficient, and can handle noisy data. However, these methods have two strong limitations: (1) a limited form of expressing the background knowledge and (2) the lack of relations other than "object-attribute" makes the concept description language inappropriate for some applications. Relational hybrid data mining methods based on first-order logic were developed to meet these challenges. In the paper they are compared with neural networks and other benchmark methods. The comparison shows several advantages of relational methods.