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LWMC: A Locally Weighted Meta-Clustering Algorithm for Ensemble Clustering

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10638))

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Abstract

The last decade has witnessed a rapid development of the ensemble clustering technique. Despite the great progress that has been made, there are still some challenging problems in the ensemble clustering research. In this paper, we aim to address two of the challenging problems in ensemble clustering, that is, the local weighting problem and the scalability problem. Specifically, a locally weighted meta-clustering (LWMC) algorithm is proposed, which is featured by two main advantages. First, it is highly efficient, due to its ability of working and voting on clusters. Second, it incorporates a locally weighted voting strategy in the meta-clustering process, which can exploit the diversity of clusters by means of local uncertainty estimation and ensemble-driven cluster validity. Experiments on eight real-world datasets demonstrate the superiority of the proposed algorithm in both clustering quality and efficiency.

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Notes

  1. 1.

    http://www.cs.nyu.edu/%7eroweis/data.html.

  2. 2.

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

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Acknowledgement

This work was supported by NSFC (61602189, 61502543 and 61573387), the Ph.D. Start-up Fund of Natural Science Foundation of Guangdong Province, China (2016A030310457), Guangdong Natural Science Funds for Distinguished Young Scholars (2016A030306014), and the Tip-Top Scientific and Technical Innovative Youth Talents of Guangdong Special Support Program (No. 2016TQ03X542).

Author information

Authors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China

    Dong Huang

  2. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China

    Chang-Dong Wang & Jian-Huang Lai

  3. Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou, China

    Chang-Dong Wang & Jian-Huang Lai

  4. Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou, China

    Chang-Dong Wang & Jian-Huang Lai

Authors
  1. Dong Huang
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  2. Chang-Dong Wang
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  3. Jian-Huang Lai
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Corresponding author

Correspondence to Chang-Dong Wang .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Huang, D., Wang, CD., Lai, JH. (2017). LWMC: A Locally Weighted Meta-Clustering Algorithm for Ensemble Clustering. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_17

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  • DOI: https://doi.org/10.1007/978-3-319-70139-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70138-7

  • Online ISBN: 978-3-319-70139-4

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