Huaxiang Zhang

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Semi-supervised clustering algorithms aim to improve the clustering accuracy under the supervisions of a limited amount of labeled data. Since kernel-based approaches, such as kernel-based fuzzy c-means algorithm (KFCM), have been successfully used in classification and clustering problems, in this paper, we propose a novel semi-supervised clustering(More)
As the better generalization ability of clusterer ensemble methods, they are widely applied to diverse domains. But now many challenges still exist. One of the drawbacks of the ensemble is, ignoring the valuable information contained in the process of training component clusterers. This paper explores a new ensemble method for cluster analysis based on(More)
Detecting community structure has become one important technique for studying complex networks. Although many community detection algorithms have been proposed, most of them focus on separated communities, where each node can belong to only one community. However, in many real-world networks, communities are often overlapped with each other. Developing(More)
Ensembles of classifiers can increase the performance of pattern recognition, and have become a hot research topic. High classification accuracy and diversity of the component classifiers are essential to obtain good generalization capability of an ensemble. We review the methods used to learn diverse classifiers, employ fuzzy clustering with deflection to(More)