Evolutionary Acquisition of Multiple TTSP Graph Patterns with Wildcards by Clustering TTSP Graphs
- Resource Type
- Conference
- Authors
- Kawasaki, Yuma; Miyahara, Tetsuhiro; Kuboyama, Tetsuji; Suzuki, Yusuke; Uchida, Tomoyuki
- Source
- 2021 IEEE 12th International Workshop on Computational Intelligence and Applications (IWCIA) Computational Intelligence and Applications (IWCIA), 2021 IEEE 12th International Workshop on. :1-8 Nov, 2021
- Subject
- Computing and Processing
Engineering Profession
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Learning systems
Knowledge acquisition
Conferences
Genetic programming
Machine learning
Data models
Data mining
evolutionary method
genetic programming
Two-Terminal Series Parallel graphs
graph structured patterns
wildcards
- Language
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 multiple TTSP graph pattern, which is a finite set of TTSP graph patterns, with wildcards and give a clustering procedure of TTSP graphs. Then we propose an evolutionary learning method for obtaining characteristic multiple TTSP graph patterns with wildcards, from positive and negative TTSP graph data by clustering positive TTSP graphs. Experimental results show that our proposed evolutionary learning method obtains characteristic multiple TTSP graph patterns with wildcards.