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Semi-supervised Affinity Propagation Clustering Algorithm Based on Stratified Combination |
Zhang Zhen Wang Bin-qiang Yi Peng Lan Ju-long |
National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450002, China |
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Abstract Considering the complexity and the accuracy, an improved affinity propagation clustering algorithm Semi-supervised Affinity Propagation clustering algorithm based on Stratified Combination (SAP-SC) is proposed. In order to make the operation simplified and easily-implemented, the proposed algorithm introduces a stratified clustering method which equally partitions the integrative clustering process into several smaller blocks. Focusing on the hard clustering data, every layer employs semi-supervised learning to conceive pair-wise constraints and maps each sub-cluster with the corresponding label. Also, assembled boosting method is utilized to weight together all layered results to improve the clustering performance. Finally, theoretical analysis and experimental results show that the algorithm can achieve both higher accuracy and better computational performance.
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Received: 31 May 2012
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Corresponding Authors:
Zhang Zhen
E-mail: zhangzhen2096@163.com
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