关键词: 核可能性C均值, 边界模糊, 类间极大惩罚项

Abstract: The traditional Kernel Possibilictic C-Means（KPCM） only consider the relationships within the class without enough attention to the distance between classes. When it comes to fuzzy boundary data, misclassification problems in boundary may easily occur due to the overlapping of the centers. To solve the above problems, this paper introduces a maximum penalty term between classes in high-dimensional feature space and the control parameter[λ]based on the KPCM. The new proposed algorithm which constructs a new objective function is called the Maximum center interval Kernel Possibilistic C-Means（MKPCM） clustering algorithm. The algorithm makes the distance between the centers maximum by the maximum penalty term between centers and through the control parameter[λ], it effectively avoids the event of too close centers or even overlaps. Numerical experimental results demonstrate its favorable performance especially in the matter with fuzzy boundary. In addition, it shows distinct advantages in the application of image segmentation compared to the traditional cluster methods.

Key words: Kernel Possibilistic C-Means（KPCM）, fuzzy boundary, maximum penalty term between centers