State Education Commission Key Laboratory for Image Processing and Intelligent Control,Institute of Image Recognition and Artificial Intelligence, Huazhong University of Science and Technology,Wuhan 430074
Abstract：A method of clustering in feature space is proposed in this paper via a kind of organization of data points. Firstly, those feature data points with higher densities which are relatively easy to be clustered are picked out as the initial seed data set. Then, the k-nearest neighbors of data in seed set are selected from the remained data points in feature space, and the data points in seed set and their k-nearest neighbors are transformed into a new space. In this space those data points are re-clustered, and the k-nearest neighbors are merged into current seed set. The above steps are iterated, and the clustering method will not terminate until there are no k-nearest points of the seed set to be found. Experimental results show that the clustering method performs better than the traditional clustering methods such as K-means, mean shift and spectral clustering.
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2010, Vol. 23 
