Abstract
Most of the existing clustering algorithms are affected seriously by noise data and high cost of time. In this paper, on the basis of CURE algorithm, a representative points clustering algorithm based on density factor and relevant degree called RPCDR is proposed. The definition of density factor and relevant degree are presented. The primary representative point whose density factor is less than the prescribed threshold will be deleted directly. New representative points can be reselected from non representative points in corresponding cluster. Moreover, the representative points of each cluster are modeled by using K-nearest neighbor method. Relevant degree is computed by comprehensive considering the correlations of objects within a cluster and between different clusters. And then whether the two clusters need to merge is judged. The theoretic experimental results and analysis prove that RPCDR has better clustering accuracy and execution efficiency.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61170190), the Nature Science Foundation of Hebei Province (No. F2015402114, F2015402070, F2015402119) and Foundation of Hebei Educational Committee (No. YQ2014014). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.
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Wu, D., Ren, J. & Sheng, L. Representative points clustering algorithm based on density factor and relevant degree. Int. J. Mach. Learn. & Cyber. 8, 641–649 (2017). https://doi.org/10.1007/s13042-015-0451-5
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DOI: https://doi.org/10.1007/s13042-015-0451-5