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Nonlinear clustering has attracted an increasing amount of attention recently. In this paper, we propose a new nonlinear clustering method based on Cluster Shrinking and Border Detection (CSBD). Unlike most existing clustering method, the CSBD method focuses on every data point rather then the cluster centers. A novel idea, namely Cluster Shrinking, is designed to transform the original nonlinear...
With the amount of data increasing rapidly, how to improve the scalability of nonlinear clustering has become a very crucial and challenging problem. In this paper, we design an efficient parallel nonlinear clustering algorithm by using a four-stage MapReduce framework. In our approach, we need to compute two quantities based on distance matrices, which, however, is difficult to compute in a MapReduce...
Multi-view clustering has become a popular clustering technique in recent years due to its ability to analyze data collected from multiple sources or represented by multiple views. In this paper, we propose a novel multi-view clustering approach termed weighted multi-view online competitive clustering (WMLCC). We simultaneously exploit the variable weighting strategy and the online competitive learning...
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