Cl-GBI: A Novel Approach for Extracting Typical Patterns from Graph-Structured Data

摘要

Graph-Based Induction (GBI) is a machine learning technique developed for the purpose of extracting typical patterns from graph-structured data by stepwise pair expansion (pair-wise chunking). GBI is very efficient because of its greedy search strategy, however, it suffers from the problem of overlapping subgraphs. As a result, some of typical patterns cannot be discovered by GBI though a beam search has been incorporated in an improved version of GBI called Beam-wise GBI (B-GBI). In this paper, improvement is made on the search capability by using a new search strategy, where frequent pairs are never chunked but used as pseudo nodes in the subsequent steps, thus allowing extraction of overlapping subgraphs. This new algorithm, called Cl-GBI (Chunking-less GBI), was tested against two datasets, the promoter dataset from UCI repository and the hepatitis dataset provided by Chiba University, and shown successful in extracting more typical patterns than B-GBI.