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

Authors: Fumiya Tokuhara; Tetsuhiro Miyahara; Tetsuji Kuboyama; Yusuke Suzuki; Tomoyuki Uchida

Addresses: Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan ' Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan ' Computer Centre, Gakushuin University, Tokyo, Japan ' Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan ' Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan

Abstract: 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.

Keywords: evolutionary method; genetic programming; outerplanar graphs; graph pattern sets.

DOI: 10.1504/IJCISTUDIES.2018.096191

International Journal of Computational Intelligence Studies, 2018 Vol.7 No.3/4, pp.270 - 288

Received: 02 Feb 2018
Accepted: 19 Jun 2018

Published online: 13 Nov 2018 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article