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One-step spectral rotation clustering with balanced constrains

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Abstract

Spectral clustering is a popular graph-based clustering method which is widely applied to pattern recognition and image segmentation. Traditional spectral clustering usually involves two separate processes: graph learning and graph-based clustering, making such a two-step strategy easily output sub-optimal performance and moreover, noisy data and imbalanced clusters could make existing clustering methods hard to meet practical necessity. To this end, one-step spectral clustering coupled with a balanced constraint is proposed to jointly optimize the robust low-dimensional representation, the spectral rotation and the cluster indicator in a unified learning framework. Specifically, dimensionality reduction is conducted by combining subspace learning and feature selection to obtain a robust low-dimensional representation. Besides, a spectral rotation mechanism is used to produce one-step clustering for reducing clustering biases, and a balanced constraint is utilized to regularize the clustering result to generate clusters with similar sizes. Moreover, an iterative optimization method is put forward to fast solve the proposed objective function. Experimental results on ten benchmark datasets compared to state-of-the-art spectral clustering methods showed that our proposed clustering method could output the balanced clusters and the competitive clustering performance simultaneously.

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

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

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Acknowledgements

This work was partially supported by the Key Program of the National Natural Science Foundation of China (Grants No: 61836016 and 61672177); the Natural Science Foundation of China (Grants No: 61876046); the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing and the Research Fund of Guangxi Key Lab of Multisource Information Mining & Security (18-A-01-01); the Guangxi “Bagui Scholar” Teams for Innovation and Research Project; Natural Science Project of Guangxi Universities (2021KY0061).

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Authors and Affiliations

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

    Guoqiu Wen, Yonghua Zhu, Linjun Chen & Shichao Zhang

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  1. Guoqiu Wen
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  2. Yonghua Zhu
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  3. Linjun Chen
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Correspondence to Shichao Zhang.

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Ten benchmark datasets used in the experiment are collected from the UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/) and the website of Feature Selection Data sets (http://featureselection.asu.edu/datasets.php)

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This article belongs to the Topical Collection: Special Issue on Web Intelligence =Artificial Intelligence in the Connected World

Guest Editors: Yuefeng Li, Amit Sheth, Athena Vakali, and Xiaohui Tao

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Wen, G., Zhu, Y., Chen, L. et al. One-step spectral rotation clustering with balanced constrains. World Wide Web 25, 259–280 (2022). https://doi.org/10.1007/s11280-021-00958-4

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