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Connected graph decomposition for spectral clustering

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Abstract

This paper proposes a new spectral clustering method based on local Principal Component Analysis (PCA) and connected graph decomposition. Specifically, our method randomly select centroids of the data set to take global structure of data points into consideration, and then uses local PCA to preserve the local structure of data points for constructing the similarity matrix. Furthermore, our method employs the connected graph decomposition to partition the resulting similarity matrix to group data points into clusters. Experimental analysis on 12 UCI data sets showed that our proposed method outperformed the state-of-the-art clustering methods in terms of clustering performance.

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  1. http://archive.ics.uci.edu/ml/index.php.

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Authors and Affiliations

  1. Guangxi Key Lab of Multi-source Information Mining and Security (MIMS), Guangxi Normal University, Guilin, 541004, People’s Republic of China

    Tao Tong, Xiaofeng Zhu & Tingting Du

  2. Institute of Natural and Mathematical Sciences, Massey University, Auckland, 0745, New Zealand

    Xiaofeng Zhu

Authors
  1. Tao Tong
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  2. Xiaofeng Zhu
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  3. Tingting Du
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Correspondence to Xiaofeng Zhu.

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This work is partially supported by the China Key Research Program (Grant No: 2016YFB1000905); the Natural Science Foundation of China (Grants No: 61876046, 61573270 and 61672177); the Project of Guangxi Science and Technology (GuiKeAD17195062); the Guangxi Natural Science Foundation (Grant No: 2015GXNSFCB139011); the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing; the Guangxi High Institutions Program of Introducing 100 High-Level Overseas Talents; and the Research Fund of Guangxi Key Lab of Multisource Information Mining & Security (18-A-01-01).

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Tong, T., Zhu, X. & Du, T. Connected graph decomposition for spectral clustering. Multimed Tools Appl 78, 33247–33259 (2019). https://doi.org/10.1007/s11042-018-6643-8

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