Acquisition of characteristic block preserving outerplanar graph patterns from positive and negative data using Genetic Programming and tree representation of graph patterns

Machine learning and data mining from graph structured data have been studied intensively. Many chemical compounds can be expressed by outerplanar graphs. We use block preserving outerplanar graph patterns having structured variables for expressing structural features of outerplanar graphs. We propose a learning method for acquiring characteristic block preserving outerplanar graph patterns from positive and negative outerplanar graph data, by using Genetic Programming and tree representation of block preserving outerplanar graph patterns. We report experimental results on applying our method to synthetic outerplanar graph data.