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An ordered clustering algorithm based on K-means and the PROMETHEE method

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Abstract

The multi-criteria decision aid (MCDA) has been a fast growing area of operational research and management science during the past two decades. The clustering problem is one of the well-known MCDA problems, in which the K-means clustering algorithm is one of the most popular clustering algorithms. However, the existing versions of the K-means clustering algorithm are only used for partitioning the data into several clusters which don’t have priority relations. In this paper, we propose a complete ordered clustering algorithm called the ordered K-means clustering algorithm, which considers the preference degree between any two alternatives. Different from the K-means clustering algorithm, we apply the relative net flow of PROMETHEE to measure the closeness of alternatives. In this case, the ordered K-means clustering algorithm can capture the different importance degrees of criteria. At last, we employ the proposed algorithm to solve a practical ordered clustering problem concerning the human development indexes. Then a comparison analysis with an existing approach is conducted to demonstrate the advantages of the ordered K-means clustering algorithm.

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Acknowledgements

The authors thank the anonymous reviewers for their helpful comments and suggestions, which have led to an improved version of this paper. The work was supported by the National Natural Science Foundation of China (Nos. 61273209, 71571123).

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Correspondence to Zeshui Xu.

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Chen, L., Xu, Z., Wang, H. et al. An ordered clustering algorithm based on K-means and the PROMETHEE method. Int. J. Mach. Learn. & Cyber. 9, 917–926 (2018). https://doi.org/10.1007/s13042-016-0617-9

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  • DOI: https://doi.org/10.1007/s13042-016-0617-9

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