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A fuzzy clustering ensemble based on cluster clustering and iterative Fusion of base clusters

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Abstract

For obtaining the more robust, novel, stable, and consistent clustering result, clustering ensemble has been emerged. There are two approaches in clustering ensemble frameworks: (a) the approaches that focus on creation or preparation of a suitable ensemble, called as ensemble creation approaches, and (b) the approaches that try to find a suitable final clustering (called also as consensus clustering) out of a given ensemble, called as ensemble aggregation approaches. The first approaches try to solve ensemble creation problem. The second approaches try to solve aggregation problem. This paper tries to propose an ensemble aggregator, or a consensus function, called as Robust Clustering Ensemble based on Sampling and Cluster Clustering (RCESCC).RCESCC algorithm first generates an ensemble of fuzzy clusterings generated by the fuzzy c-means algorithm on subsampled data. Then, it obtains a cluster-cluster similarity matrix out of the fuzzy clusters. After that, it partitions the fuzzy clusters by applying a hierarchical clustering algorithm on the cluster-cluster similarity matrix. In the next phase, the RCESCC algorithm assigns the data points to merged clusters. The experimental results comparing with the state of the art clustering algorithms indicate the effectiveness of the RCESCC algorithm in terms of performance, speed and robustness.

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Acknowledgments

This paper is extracted from a PhD thesis written by Musa Mojarad.

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Authors and Affiliations

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

    Musa Mojarad

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

    Samad Nejatian

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

    Samad Nejatian & Majid Mohammadpoor

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

    Hamid Parvin & Majid Mohammadpoor

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

    Hamid Parvin

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  1. Musa Mojarad
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  2. Samad Nejatian
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Correspondence to Samad Nejatian.

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Mojarad, M., Nejatian, S., Parvin, H. et al. A fuzzy clustering ensemble based on cluster clustering and iterative Fusion of base clusters. Appl Intell 49, 2567–2581 (2019). https://doi.org/10.1007/s10489-018-01397-x

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