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2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) (2017)
Hamamatsu, Shizuoka, Japan
July 9, 2017 to July 13, 2017
ISBN: 978-1-5386-0621-6
pp: 459-464
ABSTRACT
Knowledge acquisition from graph structured data is an important task in machine learning and data mining. TTSP (Two-Terminal Series Parallel) graphs are used as data models for electric networks and scheduling. We propose a learning method for acquiring characteristic multiple graph structured patterns by evolutionary computation using sets of TTSP graph patterns as individuals, from positive and negative TTSP graph data, in order to represent sets of TTSP graphs more precisely.
INDEX TERMS
computational complexity, data mining, data models, evolutionary computation, graph theory, knowledge acquisition, learning (artificial intelligence)
CITATION

Y. Yamagata, T. Miyahara, Y. Suzuki, T. Uchida, F. Tokuhara and T. Kuboyama, "Acquisition of Multiple Graph Structured Patterns by an Evolutionary Method Using Sets of TTSP Graph Patterns as Individuals," 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), Hamamatsu, Shizuoka, Japan, 2018, pp. 459-464.
doi:10.1109/IIAI-AAI.2017.198
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