Cutset-type Possibilistic C-means Clustering Algorithms Based on Semi-supervised Information

Abstract

Cutset-type Possibilistic C-Means clustering (C-PCM) algorithm can significantly reduce the coincident clustering phenomenon of the Possibilistic C-Means clustering (PCM) algorithm by introducing the cut-set concept into the PCM. The C-PCM also has strong robustness to noise and outliers. However, the C-PCM still suffers from the center migration problem for datasets with small targets. In order to solve this problem, a Semi-Supervised Cutset-type Possibility C-Means (SS-C-PCM) clustering algorithm is proposed by introducing the semi-supervised learning mechanism into the objective function of the C-PCM and utilizing some prior information to guide the clustering process. Meanwhile, in order to improve the segmentation efficiency and accuracy of color images, a differential evolutionary superpixel-based Semi-Supervised Cutset-type Possibilistic C-Means (desSS-C-PCM) clustering algorithm is proposed. In the desSS-C-PCM, the Differential Evolutionary Superpixel(DES) algorithm is used to obtain the spatial neighborhood information of an image, which is integrated into the objective function of the semi-supervised C-PCM to improve the segmentation quality. Simultaneously, the color histogram is used to reconstruct the new objective function to reduce the computational complexity of the algorithm. Several experiments of artificial data clustering and color image segmentation show that the proposed algorithm can effectively improve the clustering effect of datasets with small targets and the execution efficiency compared with several related algorithms.