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Consensus function based on cluster-wise two level clustering

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Abstract

The ensemble clustering tries to aggregate a number of basic clusterings with the aim of producing a more consistent, robust and well-performing consensus clustering result. The current paper wants to introduce an ensemble clustering method. The proposed method, called consensus function based on two level clustering (CFTLC), introduces a new consensus clustering where it makes a cluster clustering task through applying an average hierarchical clustering on a cluster–cluster similarity matrix obtained by an innovative similarity metric. By applying the average hierarchical clustering algorithm, a set of meta clusters has been attained. Considering each meta cluster as a consensus cluster in the consensus clustering output, it then assigns each data point to a meta cluster through defining an object-cluster similarity. Before doing anything, CFTLC converts the primary partitions into a binary cluster representation where the primary ensemble has been broken into a number of basic binary clusters (BC). CFTLC first combines the basic BCs with the maximum cluster–cluster similarity. This step is iterated as long as a predefined number of meta clusters are ready. At the subsequent step, it assigns each data point to exactly one meta cluster. The proposed method has been experimentally compared with the state of the art clustering algorithms in terms of accuracy and robustness.

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Acknowledgements

This study is supported by grants to HAR and HP. HAR is supported by UNSW Scientia Program Fellowship and is a member of the UNSW Graduate School of Biomedical Engineering.

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Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Mohammad Reza Mahmoudi

  2. Department of Statistics, Faculty of Science, Fasa University, Fasa, Fars, Iran

    Mohammad Reza Mahmoudi

  3. Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran

    Hamidreza Akbarzadeh

  4. Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran

    Hamid Parvin

  5. Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran

    Hamid Parvin

  6. Department of Electrical Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran

    Samad Nejatian

  7. Young Researchers and Elite Club, Yasooj Branch, Islamic Azad University, Yasooj, Iran

    Samad Nejatian & Vahideh Rezaie

  8. Department of Mathematics, Yasooj Branch, Islamic Azad University, Yasooj, Iran

    Vahideh Rezaie

  9. Systems Biology and Health Data Analytics Lab, The Graduate School of Biomedical Engineering, UNSW Australia, Sydney, 2052, Australia

    Hamid Alinejad-Rokny

  10. School of Computer Science and Engineering, The University of New South Wales (UNSW Sydney), Sydney, 2052, Australia

    Hamid Alinejad-Rokny

  11. Health Data Analytics Program Leader, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney, 2109, Australia

    Hamid Alinejad-Rokny

Authors
  1. Mohammad Reza Mahmoudi
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  2. Hamidreza Akbarzadeh
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  3. Hamid Parvin
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  4. Samad Nejatian
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  5. Vahideh Rezaie
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  6. Hamid Alinejad-Rokny
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Contributions

HP and HAR designed the study. HP and HAR developed hypothesis and experiments. MRM and HP wrote the manuscript; MRM, HP, and HA edited the manuscript with help from HAR; MRM, HP, HA, SN, and VR carried out the analyses, the implementation of the codes, and the statistical analyses; MRM, HP, and HA generated all figures and tables. All authors have read and approved the final version of the paper.

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Correspondence to Hamid Parvin or Hamid Alinejad-Rokny.

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Mahmoudi, M.R., Akbarzadeh, H., Parvin, H. et al. Consensus function based on cluster-wise two level clustering. Artif Intell Rev 54, 639–665 (2021). https://doi.org/10.1007/s10462-020-09862-1

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