Abstract
Most of the relations are represented by a graph structure, e.g., chemical bonding, Web browsing record, DNA sequence, Inference pattern (program trace), to name a few. Thus, efficiently finding characteristic substructures in a graph will be a useful technique in many important KDD/ML applications. However, graph pattern matching is a hard problem. We propose a machine learning technique called Graph- Based Induction (GBI) that efficiently extracts typical patterns from graph data in an approximate manner by stepwise pair expansion (pairwise chunking). It can handle general graph structured data, i.e., 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 effiectively be applied to the extraction of typical patterns from chemical compound data from which to generate classification rules, and that GBI also works as a feature construction component for other machine learning tools.
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Matsuda, T., Horiuchi, T., Motoda, H., Washio, T. (2000). Graph-Based Induction for General Graph Structured Data and Its Application to Chemical Compound Data. In: Arikawa, S., Morishita, S. (eds) Discovery Science. DS 2000. Lecture Notes in Computer Science(), vol 1967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44418-1_9
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DOI: https://doi.org/10.1007/3-540-44418-1_9
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