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Spectral clustering algorithm combining local covariance matrix with normalization

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Abstract

Affinity matrix construction is a key step in the spectral clustering. However, traditional spectral clustering methods usually ignore the intersection problem that may exist between the different clusters of data, so the resulting matrix could be unreliable. This paper proposes a new local covariance-based method to solve the above problem. Specifically, we first learn an initial affinity matrix by adding the local covariance into traditional matrix construction step, which could guarantee the obtained matrix avoids the impact of the intersection point while preserving the neighborhood relationship of data. We then employ the normalized Laplacian on the obtained matrix to further improve the clustering performance. The ACC and NMI of the proposed method increased by 6.40% and 5.33% on average compared with six classical spectral clustering methods. Experimental evaluation on eight benchmark data sets shows that the proposed method has better clustering performance.

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Notes

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

  2. http://featureselection.asu.edu/datasets.php

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Acknowledgements

This work is partially supported by the China Key Research Program (Grant No: 2016YFB1000905); the Key Program of the National Natural Science Foundation of China (Grant No: 61836016); the Natural Science Foundation of China (Grants No: 61876046, 61573270, 81701780 and 61672177); the Project of Guangxi Science and Technology (GuiKeAD17195062); the Guangxi Natural Science Foundation (Grant No: 2015GXNSFCB139011, 2017GXNSFBA198221); 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 Multi-source Information Mining & Security (18-A-01-01).

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Authors and Affiliations

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

    Tingting Du, Guoqiu Wen, Wei Zheng, Malong Tan & Yangding Li

  2. School of Journalism and Communication, Suzhou University, Suzhou, 215006, Jiangsu, People’s Republic of China

    Zhiguo Cai

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  1. Tingting Du
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  2. Guoqiu Wen
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  3. Zhiguo Cai
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Correspondence to Guoqiu Wen.

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Du, T., Wen, G., Cai, Z. et al. Spectral clustering algorithm combining local covariance matrix with normalization. Neural Comput & Applic 32, 6611–6618 (2020). https://doi.org/10.1007/s00521-018-3852-z

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