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Fuzzy Interval Number K-Means Clustering for Region Division of Pork Market

Author

Listed:
  • Xiangyan Meng

    (College of Science, Northeast Agricultural University, China)

  • Muyan Liu

    (College of Engineering, Northeast Agricultural University, China)

  • Ailing Qiao

    (College of Engineering, Northeast Agricultural University, China)

  • Huiqiu Zhou

    (College of Economics and Management, Northeast Agricultural University, China)

  • Jingyi Wu

    (College of Science, Northeast Agricultural University, China)

  • Fei Xu

    (College of Science, Northeast Agricultural University, China)

  • Qiufeng Wu

    (College of Science, Northeast Agricultural University, China)

Abstract

This article proposes a new clustering algorithm named FINK-means. First, this article converts original data into a fuzzy interval number (FIN). Second, it proves the F that denotes the collection of FINs is a lattice. Finally, it introduces a novel metric distance on the lattice F. The contrast experiments about FINK-means, k-means, and FCM algorithm are carried out on two simulated datasets and four public datasets. The results show that the FINK-means algorithm has better clustering performance on three evaluation indexes including the purity, loss cost, and silhouette coefficient. FINK-means is applied to the task of region division of pork market in China based on the daily data of pork price for different provinces of China from August 9, 2017 to August 9, 2018. The results show that regions of pork market in China was divided into five categories, namely very low, low, medium, high, and very high. Every category has been discussed as well. At last, an additional experiment about region division in Canada was carried out to prove the efficiency of FINK-means further.

Suggested Citation

  • Xiangyan Meng & Muyan Liu & Ailing Qiao & Huiqiu Zhou & Jingyi Wu & Fei Xu & Qiufeng Wu, 2020. "Fuzzy Interval Number K-Means Clustering for Region Division of Pork Market," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 12(3), pages 43-61, July.
    • Handle: RePEc:igg:jdsst0:v:12:y:2020:i:3:p:43-61
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