Abstract
A density-sensitive semi-supervised affinity propagation clustering algorithm (DS-SAP) is proposed in this chapter. The DS-SAP uses supervised information of the pairwise constraints for adjusting data points distance matrix. Then we introduce a novelty similarity metric based on the characteristics of global and local data distribution. This metric can effectively reflect the reality of data distribution. The DS-SAP clustering algorithm is based on the frame of the traditional AP algorithm and has extended data processing capacity compared to the traditional AP algorithm. Experimental results show that the new algorithm is outperforming traditional AP clustering algorithm.
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Acknowledgements
The project is supported by the National Natural Science Foundation of China (No. 61073121), National Science and Technology Support Plan Project (No. 2013BAK07B04), Natural Science Foundation of Hebei Province. China (No. F2013201170) and Medical Engineering Alternate Research Center Open Foundation of Hebei University (No. BM201102).
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Li, K., Meng, Q., Luo, S., Li, H., Wang, Q. (2014). Density-Sensitive Semi-supervised Affinity Propagation Clustering. In: Wong, W.E., Zhu, T. (eds) Computer Engineering and Networking. Lecture Notes in Electrical Engineering, vol 277. Springer, Cham. https://doi.org/10.1007/978-3-319-01766-2_21
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DOI: https://doi.org/10.1007/978-3-319-01766-2_21
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