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A multi-level consensus function clustering ensemble

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Abstract

In order to improve the performance of a clustering on a data set, a number of primary partitions are generated and stored in an ensemble and their aggregated consensus partition is used as their clustering. It is widely accepted that the consensus partition outperforms the primary partitions. In this paper, an ensemble clustering method called multi-level consensus clustering (MLCC) is proposed. To construct the MLCC, a cluster–cluster similarity matrix which is achieved by an innovative similarity metric is first generated. The mentioned cluster–cluster similarity matrix is based on a multi-level similarity metric. In fact, it can be computed in a new defined multi-level space. Then, a point–point similarity matrix which is boosted using the mentioned cluster–cluster similarity matrix is generated. The new consensus function applies an average linkage hierarchical clusterer algorithm on the mentioned point–point similarity matrix to make consensus partition. MLCC is better than traditional clustering ensembles and simple versions of clustering ensembles on traditional cluster–cluster similarity matrix. Its computational cost is not very bad too. Accuracy and robustness of the proposed method are compared with those of the modern clustering algorithms through the experimental tests. Also, time analysis is presented in the experimental results.

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Authors and Affiliations

  1. Fractional Calculus, Optimization and Algebra Research Group, Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Kim-Hung Pho

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

    Hamidreza Akbarzadeh

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

    Hamid Parvin

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

    Hamid Parvin

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

    Samad Nejatian

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

    Samad Nejatian

  7. BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia

    Hamid Alinejad-Rokny

  8. UNSW Data Science Hub, The University of New South Wales (UNSW Sydney), Sydney, NSW, 2052, Australia

    Hamid Alinejad-Rokny

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

    Hamid Alinejad-Rokny

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  1. Kim-Hung Pho
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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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Pho, KH., Akbarzadeh, H., Parvin, H. et al. A multi-level consensus function clustering ensemble. Soft Comput 25, 13147–13165 (2021). https://doi.org/10.1007/s00500-021-06092-7

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