JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2020, Vol. 55 ›› Issue (3): 107-112.doi: 10.6040/j.issn.1671-9352.4.2019.192

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Gaussian kernel fuzzy C-means clustering driven by piecewise quadratic transfer function

TANG Yi-ming1,2*, ZHANG Zheng1, LU Qi-ming1   

  1. 1. School of Computer and Information, 2. Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei University of Technology, Hefei 230601, Anhui, China
  • Published:2020-03-27

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Abstract: As for the fuzzy C-Means algorithm based on transformed data, whose conversion mode is single while the effect of non-distribution is not obvious. For these reasons, the Gaussian kernel fuzzy C-means algorithm driven by piecewise quadratic transfer function is proposed. Firstly, the strategy of transforming the original piecewise linear data is extended by the piecewise quadratic conversion function, which makes the data conversion mode more delicate, so that the same type of data is better gathered together, and different types of data keep away. Secondly, we introduce a Gaussian kernel function to map data from low-dimensional space to high-dimensional space for clustering. Finally, the piecewise quadratic conversion function and Gaussian kernel function are integrated into the framework of fuzzy clustering to form the proposed algorithm. The comparison experiments show that the proposed algorithm is significantly better than the related four algorithms.

Key words: fuzzy C-means clustering, Gaussian kernel, nonlinear data transformation, quadratic function

CLC Number: 

  • O159
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