计算机工程与应用2016，Vol.52Issue(19)：184-191,240,9.DOI:10.3778/j.issn.1002-8331.1412-0248
一种极大中心间隔的核可能性C均值聚类算法
Kernel possibilistic C-means clustering algorithm based on maximum center in-terval
摘要
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 parameterλbased on the KPCM. The new proposed algorithm which constructs a new objective function is called the Maximum center interval Kernel Pos-sibilistic 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.关键词
核可能性C均值/边界模糊/类间极大惩罚项Key words
Kernel Possibilistic C-Means(KPCM)/fuzzy boundary/maximum penalty term between centers分类
信息技术与安全科学引用本文复制引用
于晓瞳,狄岚,彭茜..一种极大中心间隔的核可能性C均值聚类算法[J].计算机工程与应用,2016,52(19):184-191,240,9.基金项目
江苏省六大人才高峰项目（No.DZXX-028）；江苏省产学研项目（No.BY2014023-33）；江南大学教师卓越工程项目（No.JGC2013145）。 （）
