Acquisition of characteristic sets of block preserving outerplanar graph patterns by a two-stage evolutionary learning method for graph pattern sets.

Knowledge acquisition from graph structured data is an important task in machine learning and data mining. Block preserving outerplanar graph patterns are graph structured patterns having structured variables and are suited to represent characteristic graph structures of graph data modelled as outerplanar graphs. We propose a learning method for acquiring characteristic sets of block preserving outerplanar graph patterns by a two-stage evolutionary learning method for graph pattern sets as individuals, from positive and negative outerplanar graph data, in order to represent characteristic graph structures more concretely.