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Because the amount of data accumulated by controllable nuclear fusion devices is too large, a large amount of data has not been fully exploited. For such big data processing, machine learning can play an important role. Therefore, this paper uses the spectral clustering method to realize the automatic processing of data, which can easily and quickly find the pattern information contained in the data, and the discovery of these patterns is of great significance for improving plasma confinement and understanding plasma physics. In addition, this paper applies the spectral clustering method on EAST tokamak device electron cyclotron radiation imaging (ECEI), one-dimensional diagnostic system electron cyclotron radiation (ECE), magnetic probe, soft X-ray (SXR), fast radiation ( Fast bolometer) and other different diagnostic systems, the sawtooth pattern was identified, the migration of the spectral clustering method was verified, and the problems of poor data processing migration in supervised learning and the need to rely on a large number of labeled data were solved. Finally, this paper uses ECEI and magnetic probe data to discover a possible new mode in the time domain and frequency domain respectively, which provides a new idea for the exploration of new modes.

Implementation of spectral clustering algorithm for automatic identification of plasma coherence patterns