Wei Huiqin, Chen Long, Guo Li
Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau 999078, China.
Entropy (Basel). 2018 Apr 12;20(4):273. doi: 10.3390/e20040273.
Ensemble clustering combines different basic partitions of a dataset into a more stable and robust one. Thus, cluster ensemble plays a significant role in applications like image segmentation. However, existing ensemble methods have a few demerits, including the lack of diversity of basic partitions and the low accuracy caused by data noise. In this paper, to get over these difficulties, we propose an efficient fuzzy cluster ensemble method based on Kullback-Leibler divergence or simply, the KL divergence. The data are first classified with distinct fuzzy clustering methods. Then, the soft clustering results are aggregated by a fuzzy KL divergence-based objective function. Moreover, for image segmentation problems, we utilize the local spatial information in the cluster ensemble algorithm to suppress the effect of noise. Experiment results reveal that the proposed methods outperform many other methods in synthetic and real image-segmentation problems.
集成聚类将数据集的不同基本划分组合成一个更稳定、更健壮的划分。因此，聚类集成在图像分割等应用中发挥着重要作用。然而，现有的集成方法存在一些缺点，包括基本划分缺乏多样性以及数据噪声导致的低准确性。在本文中，为了克服这些困难，我们提出了一种基于库尔贝克-莱布勒散度（简称KL散度）的高效模糊聚类集成方法。首先使用不同的模糊聚类方法对数据进行分类。然后，通过基于模糊KL散度的目标函数聚合软聚类结果。此外，对于图像分割问题，我们在聚类集成算法中利用局部空间信息来抑制噪声的影响。实验结果表明，在合成图像和真实图像分割问题中，所提出的方法优于许多其他方法。
