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.
With the continuous development of Internet technology, the national colleges are actively building own teaching platform and the instructional resource database. And the construction of the instructional resource platform has played a good role in improve the teaching quality. Under the background of rapid development of cloud computing, using cloud computing technology to build the cloud instructional resources platform is a new topic in educational fields. This article aims at using hadoop cloud framework to build a opening, flexible and feasible cloud instructional resources platform.
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