基于自步数据重构正则化的模糊C均值聚类算法改进

为了有效降低模糊C均值算法对奇异值和噪声点的敏感性，本文提出一种自步数据重构正则化模糊C均值聚类算法。传统算法是在C均值算法的目标函数中引入加权参数来实现对数据的模糊性划分，而本文提出的方法则是通过对C均值的目标函数进行数据重构正则化来实现，并以自步学习的方式逐步对数据点进行聚类。实验结果表明，本文算法在模拟数据、实际数据以及在图像分割中都能显著降低算法对奇异值和噪声数据的敏感性，聚类更为准确高效。

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

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.