An Efficient Clustering Algorithm for Irregularly Shaped Clusters

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概要

• 論文の詳細を見る

• To detect the natural clusters for irregularly shaped data distribution is a difficult task in pattern recognition. In this study, we propose an efficient clustering algorithm for irregularly shaped clusters based on the advantages of spectral clustering and Affinity Propagation (AP) algorithm. We give a new similarity measure based on neighborhood dispersion analysis. The proposed algorithm is a simple but effective method. The experimental results on several data sets show that the algorithm can detect the natural clusters of input data sets, and the clustering results agree well with that of human judgment.

• （社）電子情報通信学会の論文
• 2010-02-01

著者

• Cao Yong

  School Of Pharmacy Second Military Medical University

• Yang Fan

  School Of Computer Science And Engineering University Of Electronic Science And Technology Of China

• TANG DongMing

  School of Computer Science and Engineering, University of Electronic Science and Technology of China

• ZHU QingXin

  School of Computer Science and Engineering, University of Electronic Science and Technology of China

• Zhu Qingxin

  School Of Computer Science And Engineering University Of Electronic Science And Technology Of China

• Tang Dongming

  School Of Computer Science And Engineering University Of Electronic Science And Technology Of China

• Cao Yong

  School Of Computer Science And Engineering University Of Electronic Science And Technology Of China

• Cao Yong

  School Of Chemical Engineering And The Environment Beijing Institute Of Technology

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