Improvement of Fuzzy C-Means Clustering Algorithm Based on Self-paced Data Reconstruction Regularization

Abstract

Abstract: In order to reduce the sensitivity of fuzzy C-means clustering algorithm for outliers and noise data points, a self-paced data reconstruction is proposed. Traditional fuzzy C-means algorithm realizes fuzzification of memberships by introducing a weighting parameter into the objective function of the C-means clustering. This paper achieves fuzzification of memberships through regularization of hard C-means clustering by data reconstruction. In addition, the proposed algorithm gradually carries out the clustering of data points in a self-paced manner. Experimental results show that the algorithm can significantly reduce the sensitivity to singular value and noise data in simulation data, actual data and image segmentation, and clustering is more accurate and efficient.

Key words: fuzzy C-means, clustering partition, self-paced learning, data reconstruction regularization

CLC Number:

TP391

CHEN Yi-jun, CAO Luo-wei, DU Yu-qian. Improvement of Fuzzy C-Means Clustering Algorithm Based on Self-paced Data Reconstruction Regularization[J]. Computer and Modernization, 2020, 0(06): 120-.

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