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 25 articles , 1 standard article )

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  1. Calder, Jeff: A direct verification argument for the Hamilton-Jacobi equation continuum limit of nondominated sorting (2016)
  2. Truong, Duy Tin; Battiti, Roberto: A flexible cluster-oriented alternative clustering algorithm for choosing from the Pareto front of solutions (2015)
  3. Hanwell, D.; Mirmehdi, M.: QUAC: quick unsupervised anisotropic clustering (2014)
  4. Li, Yangyang; Feng, Shixia; Zhang, Xiangrong; Jiao, Licheng: SAR image segmentation based on quantum-inspired multiobjective evolutionary clustering algorithm (2014)
  5. Sabo, Miroslav: Consensus clustering with differential evolution (2014)
  6. Brusco, Michael; Doreian, Patrick; Steinley, Douglas; Satornino, Cinthia B.: Multiobjective blockmodeling for social network analysis (2013)
  7. Li, Yangyang; Li, Peidao; Wu, Bo; Jiao, Lc; Shang, Ronghua: Kernel clustering using a hybrid memetic algorithm (2013)
  8. Naldi, M.C.; Carvalho, A.C.P.L.F.; Campello, R.J.G.B.: Cluster ensemble selection based on relative validity indexes (2013)
  9. Corne, David; Dhaenens, Clarisse; Jourdan, Laetitia: Synergies between operations research and data mining: the emerging use of multi-objective approaches (2012)
  10. Breaban, Mihaela; Luchian, Henri: A unifying criterion for unsupervised clustering and feature selection (2011)
  11. Shi, Chuan; Yan, Zhen-Yu; Pan, Xin; Cai, Ya-Nan; Wu, Bin: A posteriori approach for community detection (2011)
  12. Tan, Swee Chuan; Ting, Kai Ming; Teng, Shyh Wei: A general stochastic clustering method for automatic cluster discovery (2011)
  13. Bandyopadhyay, S.; Baragona, R.; Maulik, U.: Clustering multivariate time series by genetic multiobjective optimization (2010)
  14. Demir, G.Nildem; Uyar, A.Şima; Gündüz-Öğüdücü, Şule: Multiobjective evolutionary clustering of web user sessions: a case study in web page recommendation (2010)
  15. Fernandez, Eduardo; Navarro, Jorge; Bernal, Sergio: Handling multicriteria preferences in cluster analysis (2010)
  16. Nascimento, Mariá C.V.; Toledo, Franklina M.B.; de Carvalho, André C.P.L.F.: Investigation of a new GRASP-based clustering algorithm applied to biological data (2010)
  17. Ramdane, Chafika; Meshoul, Souham; Batouche, Mohamed; Kholladi, Mohamed-Khireddine: A quantum evolutionary algorithm for data clustering (2010)
  18. Saha, Sriparna; Bandyopadhyay, Sanghamitra: A symmetry based multiobjective clustering technique for automatic evolution of clusters (2010)
  19. Saha, Sriparna; Bandyopadhyay, Sanghamitra: A new multiobjective clustering technique based on the concepts of stability and symmetry (2010)
  20. Saha, Sriparna; Maulik, Ujjwal: Use of symmetry and stability for data clustering (2010)

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