Graph-based induction and its applications

A machine learning technique called Graph-based induction (GBI) efficiently extracts typical patterns from graph data by stepwise pair expansion (pairwise chunking). In this paper, we introduce GBI for general graph structured data, which can handle directed/undirected, colored/uncolored graphs with/without (self) loop and with colored/uncolored links. We show that its time complexity is almost linear with the size of graph. We, further, show that GBI can effectively be applied to the extraction of typical patterns from DNA sequence data and organochlorine compound data from which are to be generated classification rules, and that GBI also works as a feature construction component for other machine learning tools.