MOCK

Exploiting the trade-off-the benefits of multiple objectives in data clustering In previous work, we have proposed a novel approach to data clustering based on the explicit optimization of a partitioning with respect to two complementary clustering objectives. Here, we extend this idea by describing an advanced multiobjective clustering algorithm, MOCK, with the capacity to identify good solutions from the Pareto front, and to automatically determine the number of clusters in a data set. The algorithm has been subject to a thorough comparison with alternative clustering techniques and we briefly summarize these results. We then present investigations into the mechanisms at the heart of MOCK: we discuss a simple example demonstrating the synergistic effects at work in multiobjective clustering, which explain its superiority to single-objective clustering techniques, and we analyse how MOCK’s Pareto fronts compare to the performance curves obtained by single-objective algorithms run with a range of different numbers of clusters specified.


References in zbMATH (referenced in 40 articles , 1 standard article )

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  1. Dhaenens, Clarisse; Jourdan, Laetitia: Metaheuristics for data mining (2019)
  2. He, Zhenfeng; Yu, Chunyan: Clustering stability-based evolutionary K-means (2019)
  3. Liu, Cong; Chen, Qianqian; Chen, Yingxia; Liu, Jie: A fast multiobjective fuzzy clustering with multimeasures combination (2019)
  4. Chakraborty, Shouvik; Mali, Kalyani: Application of multiobjective optimization techniques in biomedical image segmentation -- a study (2018)
  5. Saha, Sriparna: Enhancing point symmetry-based distance for data clustering (2018)
  6. Bilal, Saoud; Abdelouahab, Moussaoui: Evolutionary algorithm and modularity for detecting communities in networks (2017)
  7. Hämäläinen, Joonas; Jauhiainen, Susanne; Kärkkäinen, Tommi: Comparison of internal clustering validation indices for prototype-based clustering (2017)
  8. Calder, Jeff: A direct verification argument for the Hamilton-Jacobi equation continuum limit of nondominated sorting (2016)
  9. Luo, Juanjuan; Jiao, Licheng; Shang, Ronghua; Liu, Fang: Learning simultaneous adaptive clustering and classification via MOEA (2016)
  10. Truong, Duy Tin; Battiti, Roberto: A flexible cluster-oriented alternative clustering algorithm for choosing from the Pareto front of solutions (2015)
  11. Yang, Dongdong; Yang, Hui; Fei, Rong: An efficient SAR image segmentation framework using transformed nonlocal mean and multi-objective clustering in kernel space (2015)
  12. Zhou, Xu; Liu, Yanheng; Li, Bin; Sun, Geng: Multiobjective biogeography based optimization algorithm with decomposition for community detection in dynamic networks (2015)
  13. Hanwell, D.; Mirmehdi, M.: QUAC: quick unsupervised anisotropic clustering (2014) ioport
  14. Li, Yangyang; Feng, Shixia; Zhang, Xiangrong; Jiao, Licheng: SAR image segmentation based on quantum-inspired multiobjective evolutionary clustering algorithm (2014)
  15. Sabo, Miroslav: Consensus clustering with differential evolution (2014)
  16. Brusco, Michael; Doreian, Patrick; Steinley, Douglas; Satornino, Cinthia B.: Multiobjective blockmodeling for social network analysis (2013)
  17. Li, Yangyang; Li, Peidao; Wu, Bo; Jiao, Lc; Shang, Ronghua: Kernel clustering using a hybrid memetic algorithm (2013) ioport
  18. Naldi, M. C.; Carvalho, A. C. P. L. F.; Campello, R. J. G. B.: Cluster ensemble selection based on relative validity indexes (2013)
  19. Shelokar, Prakash; Quirin, Arnaud; Cordón, Óscar: A multiobjective evolutionary programming framework for graph-based data mining (2013) ioport
  20. Xia, Hu; Zhuang, Jian; Yu, Dehong: Novel soft subspace clustering with multi-objective evolutionary approach for high-dimensional data (2013)

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