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Combined constraint-based with metric-based in semi-supervised clustering ensemble

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Abstract

Recently, both semi-supervised clustering and cluster ensemble have received tremendous attention due to their accurate and reliable performance. There are mainly two kinds of existing semi-supervised clustering algorithms called constraint-based and metric-based. In this paper, we present a semi-supervised clustering ensemble approach which takes both pairwise constraints and metric measure into account. Firstly, under the assistance of supervised information included pairwise constraints and labeled data, the approach generates different base clustering partitions respectively using constraint-based semi-supervised clustering and metric-based semi-supervised clustering, in which the latter develops a new metric function. Given the spatial particularity of image pixels, the metric considers spatial distribution of surrounding pixels besides inherent features of pixels in the process of image feature extraction. And then the target clustering is obtained by integrating those base clustering partitions into an ensemble function. Finally, we conduct experimental verification on general data sets and image data sets, and compare clustering performance of our approach with those of other approaches. Both theoretical analysis and experimental results demonstrate that the proposed method produces considerable improvement in clustering accuracy and yields superior clustering results over a number of representative clustering methods.

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Acknowledgements

We would like to thank the anonymous reviewers for their insightful comments and suggestions to significantly improve the quality of this paper. This research reported in this paper is supported by the National Natural Science Foundation of China (Nos. 61165009, 61663004, 61262005, 61363035, 61365009), the Guangxi Natural Science Foundation (2016GXNSFAA380146, 2014GXNSFAA118368), the Direct Fund of Guangxi Key Lab of Multi-source information Mining and Security (16-A-03-02),the Guangxi “Bagui Scholar” Teams for Innovation and Research Project, Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing.

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Authors and Affiliations

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

    Siting Wei, Zhixin Li & Canlong Zhang

  2. College of Computer Science and Information Technology, Guangxi Normal University, Guilin, 541004, China

    Siting Wei, Zhixin Li & Canlong Zhang

  3. Guangxi Experiment Center of Information Science, Guilin, 541004, China

    Zhixin Li & Canlong Zhang

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  1. Siting Wei
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  2. Zhixin Li
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  3. Canlong Zhang
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Correspondence to Zhixin Li.

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Wei, S., Li, Z. & Zhang, C. Combined constraint-based with metric-based in semi-supervised clustering ensemble. Int. J. Mach. Learn. & Cyber. 9, 1085–1100 (2018). https://doi.org/10.1007/s13042-016-0628-6

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  • DOI: https://doi.org/10.1007/s13042-016-0628-6

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