Mining Patterns from Structured Data by Beam-Wise Graph-Based Induction

Graph-Based Induction (GBI) extracts typical patterns from graph data by stepwise pair expansion (pairwise chunking). It is very efficient because of its greedy search strategy but at the same time it suffers from the incompleteness of search. Improvement is made on its search capability without imposing much computational complexity by 1) incorporating a beam search, 2) using a different evaluation function to extract patterns that are more discriminatory than those simply occurring frequently, and 3) adopting canonical labeling to enumerate identical patterns accurately. This new algorithm, now called Beam-wise GBI, B-GBI for short, was tested against the promoter dataset from UCI repository and shown successful in extracting discriminatory substructures. Effect of beam width on the number of discovered attributes and predictive accuracy was evaluated. The best result obtained by this approach was better than the previously best known result. B-GBI was then applied to a real-world data, Hepatitis dataset provided by Chiba University. Our very preliminary results indicate that B-GBI can actually handle graphs with a few thousands nodes and extract discriminatory patterns.