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Research Article

Data clustering and imputing using a two-level multi-objective genetic algorithm (GA): A case study of maintenance cost data for tunnel fans

ORCID Icon, & | (Reviewing editor)
Article: 1513304 | Received 09 Apr 2018, Accepted 13 Aug 2018, Published online: 11 Sep 2018

References

  • Bezdek, J. C . (1973). Fuzzy mathematics in pattern classification. Ph. D. Dissertation, Applied Mathematics, Cornell University.
  • Bezdek, J. C. , Ehrlich, R. , & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences , 10(2–3), 191–203. doi:10.1016/0098-3004(84)90020-7
  • Cai, L. , Yao, X. , He, Z. , & Liang, X. (2010). K-means clustering analysis based on immune genetic algorithm. Paper presented at the World Automation Congress (WAC), 2010 (pp. 413–418).
  • Cordón, O. , Herrera, F. , Gomide, F. , Hoffmann, F. , & Magdalena, L. (2001). Ten years of genetic fuzzy systems: Current framework and new trends. Paper presented at the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, 2001 (pp. 1241–1246).
  • Deb, K. (2014). Multi-objective optimization. In Search methodologies (pp. 403-449). Boston, MA: Springer.
  • Deb, K. , Pratap, A. , Agarwal, S. , & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation , 6(2), 182–197. doi:10.1109/4235.996017
  • Ding, C. , Cheng, Y. , & He, M. (2007). Two-level genetic algorithm for clustered traveling salesman problem with application in large-scale TSPs. Tsinghua Science & Technology , 12(4), 459–465. doi:10.1016/S1007-0214(07)70068-8
  • Hadavandi, E. , Shavandi, H. , & Ghanbari, A. (2011). An improved sales forecasting approach by the integration of genetic fuzzy systems and data clustering: Case study of printed circuit board. Expert Systems with Applications , 38(8), 9392–9399. doi:10.1016/j.eswa.2011.01.132
  • Handl, J. , & Knowles, J. (2004). Evolutionary multiobjective clustering. Paper presented at the International Conference on Parallel Problem Solving from Nature (pp. 1081–1091).
  • Handl, J. , & Knowles, J. (2007). An evolutionary approach to multiobjective clustering. IEEE Transactions on Evolutionary Computation , 11(1), 56–76. doi:10.1109/TEVC.2006.877146
  • Hartigan, J. A. , & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society Series C (Applied Statistics) , 28(1), 100–108.
  • Hathaway, R. J. , & Bezdek, J. C. (2001). Fuzzy c-means clustering of incomplete data. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) , 31(5), 735–744. doi:10.1109/3477.956035
  • Izakian, H. , & Abraham, A. (2011). Fuzzy c-means and fuzzy swarm for fuzzy clustering problem. Expert Systems with Applications , 38(3), 1835–1838. doi:10.1016/j.eswa.2010.07.112
  • Jain, A. K. , Murty, M. N. , & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys (CSUR) , 31(3), 264–323. doi:10.1145/331499.331504
  • Kwon, S. H. (1998). Cluster validity index for fuzzy clustering. Electronics Letters , 34(22), 2176. doi:10.1049/el:19981523
  • Lei, T., Jia, X., Zhang, Y., He, L., Meng, H., & Nandi, A. K . (2018). Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering. IEEE Transactions on Fuzzy Systems.
  • Likas, A. , Vlassis, N. , & Verbeek, J. J. (2003). The global k-means clustering algorithm. Pattern Recognition , 36(2), 451–461. doi:10.1016/S0031-3203(02)00060-2
  • Lobato, F. , Sales, C. , Araujo, I. , Tadaiesky, V. , Dias, L. , Ramos, L. , & Santana, A. (2015). Multi-objective genetic algorithm for missing data imputation. Pattern Recognition Letters , 68, 126–131. doi:10.1016/j.patrec.2015.08.023
  • Lohrmann, C. , & Luukka, P. (2018). A novel similarity classifier with multiple ideal vectors based on k-means clustering. Decision Support Systems , 111, 27–37. doi:10.1016/j.dss.2018.04.003
  • Piroozfard, H. , Wong, K. Y. , & Wong, W. P. (2018). Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm. Resources, Conservation and Recycling , 128, 267–283. doi:10.1016/j.resconrec.2016.12.001
  • Runkler, T. A. (2012). Data analytics: Models and algorithms for intelligent data analysis . Germany: Springer Science & Business Media.
  • Saha, S. , & Bandyopadhyay, S. (2010). A symmetry based multiobjective clustering technique for automatic evolution of clusters. Pattern Recognition , 43(3), 738–751. doi:10.1016/j.patcog.2009.07.004
  • Schafer, J. L. , & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods , 7(2), 147. doi:10.1037/1082-989X.7.2.147
  • Sortrakul, N. , Nachtmann, H. L. , & Cassady, C. R. (2005). Genetic algorithms for integrated preventive maintenance planning and production scheduling for a single machine. Computers in Industry , 56(2), 161–168. doi:10.1016/j.compind.2004.06.005
  • Thomassey, S. , & Happiette, M. (2007). A neural clustering and classification system for sales forecasting of new apparel items. Applied Soft Computing , 7(4), 1177–1187. doi:10.1016/j.asoc.2006.01.005
  • Wikaisuksakul, S. (2014). A multi-objective genetic algorithm with fuzzy c-means for automatic data clustering. Applied Soft Computing , 24, 679–691. doi:10.1016/j.asoc.2014.08.036
  • Xu, R. , & Wunsch, D. (2005). Survey of clustering algorithms. IEEE Transactions on Neural Networks , 16(3), 645–678. doi:10.1109/TNN.2005.845141
  • Xu, R. , & Wunsch, D. (2008). Clustering . United States of America: John Wiley & Sons.
  • Zheng, D. X. , Ng, S. T. , & Kumaraswamy, M. M. (2004). Applying a genetic algorithm-based multiobjective approach for time-cost optimization. Journal of Construction Engineering and Management , 130(2), 168–176. doi:10.1061/(ASCE)0733-9364(2004)130:2(168)