Abstract
The ordered clustering problem in the context of multicriteria decision aid has been increasingly examined in management science and operational research during the past few years. However, the existing clustering algorithms may not provide an exact suggestion for a partition number for decision makers by using the diagram method. In addition, these methods may be not appropriate for real-life problems under big data environments due to their high computational complexities. Therefore, we propose a new clustering algorithm called the ordered fuzzy c-means clustering algorithm (OFCM) to overcome the abovementioned deficiencies. Different from the classical fuzzy c-means clustering algorithm, we use the net outranking flow of PROMETHEE and validity measures for clustering to establish a new objective function, whose properties are mathematically justified as well. Finally, we employ OFCM to solve a practical ordered clustering problem concerning the human development indexes. A comparison analysis with existing approaches is also conducted to validate the efficiency of OFCM.
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References
Belacel N (2000) Multicriteria assignment method PROAFTN: methodology and medical application. Eur J Oper Res 125:175–183
Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York pp 44–47
Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10:191–203
Boujelben MA (2016) A unicriterion analysis based on the PROMETHEE principles for multicriteria ordered clustering. Omega 69:126–140
Brans JP, Mareschal B (2004) PROMETHEE Methods. In Multiple criteria decision analysis state of the art surveys. Springer, New York pp 163–186
Brans JP, Vincke P, Mareschal B (1986) How to select and how to rank projects: the Promethee method. Eur J Oper Res 24:228–238
Chen L, Xu Z, Wang H, Liu S (2016) An ordered clustering algorithm based on K-means and the PROMETHEE method. Int J Mach Learn Cybern 1:1–10
De Smet Y, Gilbart F (2001) A cluster definition method for country risk problem. Tech Rep SMG IS-MG 13: 11–20
De Smet Y, Nemery P, Selvaraj R (2012) An exact algorithm for the multicriteria ordered clustering problem. Omega 40:861–869
Doumpos M, Zopounidis C (2004) A multicriteria classification approach based on pairwise comparisons. Eur J Oper Res 158:378–389
Dunn JC (2008) Well-Separated Clusters and Optimal Fuzzy Partitions. Journal of Cybernetics 4:95–104
Eppe S, Roland J, De Smet Y (2014) On the use of valued action profiles for relational multi-criteria clustering. Int J Multicri Decis Mak 4:201–205
Ishizaka A, Nemery P (2014) Assigning machines to incomparable maintenance strategies with ELECTRE-SORT. Omega 47:45–59
Meyer P, Olteanu AL (2013) Formalizing and solving the problem of clustering in MCDA. Eur J Oper Res 227:494–502
Michalowski W, Rubin S, Slowinski R, Wilk S (2001) Triage of the child with abdominal pain: a clinical algorithm for emergency patient management. Paediatrics Child Health 6:23–28
Militello C et al (2015) Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy c-Means clustering. Int J Imaging Syst Technol 25:213–225
Mumpower JL, Livingston S, Lee TJ (1987) Expert judgments of political riskiness. J Forecast 6:51–65
Nemery P, Lamboray C (2008) FLOWSORT: a flow-based sorting method with limiting or central profiles. TOP 16:90–113
Org Z (1991) The determinants of country risk ratings. J Int Bus Stud 22:135–142
Pacheco J, Casado S, Angel-Bello F, ÁLvarez A (2013) Bi-objective feature selection for discriminant analysis in two-class classification. Knowl Based Syst 44:57–64
Shen L, Tay FEH, Qu L, Shen Y (2000) Fault diagnosis using rough sets. Theory Comput Ind 43:61–72
Siskos Y, Grigoroudis E, Zopounidis C, Saurais O (1998) Measuring customer satisfaction using a collective preference disaggregation model. J Global Optim 12:175–195
Wang XZ, Wang YD, Wang LJ (2004) Improving fuzzy c -means clustering based on feature-weight learning. Pattern Recogn Lett 25:1123–1132
Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 13:841–847
Xu Z, Wu J (2010) Intuitionistic fuzzy C-means clustering algorithm. J Syst Eng Electron 21:580–590
Yeung D, Wang XZ (2002) Improving performance of similarity-based clustering by feature weight learning. IEEE Trans Pattern Anal Mach Intell 24:556–561
Yu W (1992) ELECTRE TRI: aspects méthodologiques et manuel d’utilisation. PhD thesis, Université Paris-Dauphine :135–140
Zarinbal M, Fazel Zarandi MH, Turksen IB (2014) Interval Type-2 Relative Entropy Fuzzy C-Means clustering. Inf Sci 260:49–72
Zhang Z, Gao G, Tian Y (2015) Multi-kernel multi-criteria optimization classifier with fuzzification and penalty factors for predicting biological activity. Knowl-Based Syst 89:301–313
Zheng Y, Byeungwoo J, Xu D, Wu QMJ, Hui Z (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst 28:4024–4028
Zhu H, Tsang E, Wang XZ et al (2017) Monotonic classification extreme learning machine. Neurocomputing 225:205–213
Zopounidis C, Doumpos M (1999) Business failure prediction using the UTADIS multicriteria analysis method. J Oper Res Soc 50:1138–1148
Zopounidis C, Doumpos M (2002) Multicriteria classification and sorting methods: a literature review. Eur J Oper Res 138:229–246
Zopounidis C, Doumpos M, Zanakis S (1999) Stock evaluation using a preference disaggregation methodology. Decis Sci 30:313–336
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This paper was supported by the National Natural Science Foundation of China (No. 51609254) and the Specific Fund (CQZ-2014001) for the Industrial Site in the City of Tangshan.
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Bai, C., Zhang, R., Qian, L. et al. An ordered clustering algorithm based on fuzzy c-means and PROMETHEE. Int. J. Mach. Learn. & Cyber. 10, 1423–1436 (2019). https://doi.org/10.1007/s13042-018-0824-7
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DOI: https://doi.org/10.1007/s13042-018-0824-7