An Efficient Graph-Based Multi-Relational Data Mining Algorithm

Abstract

Multi-relational data mining can be categorized into graph-based and logic-based approaches. In this paper, we propose some optimizations for mining graph databases with Subdue, which is one of the earliest and most effective graph-based relational learning algorithms. The optimizations improve the subgraph isomorphism computation and reduce the numbers of subgraph isomorphism testing, which are the major source of complexity in Subdue. Experimental results indicate that the improved algorithm is much more efficient than the original one.