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A robust spectral clustering algorithm based on grid-partition and decision-graph

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Abstract

Spectral clustering (SC) transforms the dataset into a graph structure, and then finds the optimal subgraph by the way of graph-partition to complete the clustering. However, SC algorithm constructs the similarity matrix and feature decomposition for overall datasets, which needs high consumption. Secondly, k-means is taken at the clustering stage and it selects the initial cluster centers randomly, which leads to the unstable performance. Thirdly, SC needs prior knowledge to determine the number of clusters. To deal with these issues, we propose a robust spectral clustering algorithm based on grid-partition and decision-graph (PRSC) to reduce the amount of calculation and improve the clustering efficiency. In addition, a decision-graph method is added to identify the cluster centers quickly to improve the algorithm stability without any prior knowledge. A numerical experiments validate that PRSC algorithm can effectively improve the efficiency of SC. It can quickly obtain the stable performance without any prior knowledge.

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Acknowledgements

This work is supported by the National Natural Science Foundations of China (No. 61672522, No. 61976216),in part of the Cyan and Blue project of Jiangsu of China(2020).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Lijuan Wang, Shifei Ding, Yanru Wang & Ling Ding

  2. Mine Digitization Engineering Research Center of Ministry of Education of the People’s Republic of China, Xuzhou, 221116, China

    Shifei Ding

  3. School of Information and Electrical Engineering, Xuzhou College of Industrial Technology, Xuzhou, 221400, China

    Lijuan Wang

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  1. Lijuan Wang
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  2. Shifei Ding
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  3. Yanru Wang
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  4. Ling Ding
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Correspondence to Shifei Ding.

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Wang, L., Ding, S., Wang, Y. et al. A robust spectral clustering algorithm based on grid-partition and decision-graph. Int. J. Mach. Learn. & Cyber. 12, 1243–1254 (2021). https://doi.org/10.1007/s13042-020-01231-2

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  • DOI: https://doi.org/10.1007/s13042-020-01231-2

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