[Table of Contents]

Plasma and Fusion Research

Volume 5, 034 (2010)

Regular Articles

Studies of MHD Stability Using Data Mining Technique in Helical Plasmas
Satoshi YAMAMOTO, David PRETTY1), Boyd BLACKWELL2), Kazunobu NAGASAKI, Hiroyuki OKADA, Fumimichi SANO, Tohru MIZUUCHI, Shinji KOBAYASHI, Katsumi KONDO3), Ruben JIMÉNEZ-GÓMEZ1), Enrique ASCASÍBAR1), Kazuo TOI5) and Satoshi OHDACHI5)
Institute of Advanced Energy, Kyoto University, Uji 611-0011, Japan
Laboratorio Nacional de Fusión, Asociación Euratom-CIEMAT, Madrid 28040, Spain
Australian National University, Canberra ACT 0200, Australia
Graduate School of Energy Science, Kyoto University, Uji 611-0011, Japan
National Institute for Fusion Science, Toki 509-5292, Japan
(Received 5 December 2008 / Accepted 19 November 2009 / Published 1 October 2010)


Data mining techniques, which automatically extract useful knowledge from large datasets, are applied to multichannel magnetic probe signals of several helical plasmas in order to identify and classify MHD instabilities in helical plasmas. This method is useful to find new MHD instabilities as well as previously identified ones. Moreover, registering the results obtained from data mining in a database allows us to investigate the characteristics of MHD instabilities with parameter studies. We introduce the data mining technique consisted of pre-processing, clustering and visualizations using results from helical plasmas in H-1 and Heliotron J. We were successfully able to classify the MHD instabilities using the criterion of phase differences of each magnetic probe and identify them as energetic-ion-driven MHD instabilities using parameter study in Heliotron J plasmas.


MHD stability, data mining, magnetic probe, energetic-ion-driven MHD instability, helical plasma

DOI: 10.1585/pfr.5.034


  • [1] J. Han and M. Kamber, Data Mining: Concepts and Techniques (Morgan Kaufmann, 2001).
  • [2] W. Frawley and G. Piatetsky-Shairo and C. Matheus, Knowledge Discovery in Database: An Overview (AI Magazine, Fall, 1992).
  • [3] D. Hand, H. Mannila and P. Smyth, Principles of Data Mining (MIT Press, Cambridge, MA, 2001).
  • [4] B.D. Blackwell, Phys. Plasmas 8, 2238 (2001).
  • [5] B.D. Blackwell et al., Contribution paper of Fusion Energy Conference, EX/P6-3 (2006).
  • [6] D.G. Pretty and B. Blackwell, Comput. Physics Comm. 180, 1768 (2009).
  • [7] D.G. Pretty, PhD thesis, Australian National University (2007).
  • [8] T. Obiki et al., Nucl. Fusion 40, 261 (2001).
  • [9] I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann, 2nd edition, 2005).
  • [10] S. Yamamoto et al., Fusion Sci. Technol. 51, 92 (2007).

This paper may be cited as follows:

Satoshi YAMAMOTO, David PRETTY, Boyd BLACKWELL, Kazunobu NAGASAKI, Hiroyuki OKADA, Fumimichi SANO, Tohru MIZUUCHI, Shinji KOBAYASHI, Katsumi KONDO, Ruben JIMÉNEZ-GÓMEZ, Enrique ASCASÍBAR, Kazuo TOI and Satoshi OHDACHI, Plasma Fusion Res. 5, 034 (2010).