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Fuzzy clustering of probability density functions

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  • Thao Nguyentrang
  • Tai Vovan

Abstract

Basing on L1-distance and representing element of cluster, the article proposes new three algorithms in Fuzzy Clustering of probability density Functions (FCF). They are hierarchical approach, non-hierarchical approach and the algorithm to determine the optimal number of clusters and the initial partition matrix to improve the qualities of established clusters in non-hierarchical approach. With proposed algorithms, FCF has more advantageous than Non-fuzzy Clustering of probability density Functions. These algorithms are applied for recognizing images from Texture and Corel database and practical problem about studying and training marks of students at an university. Many Matlab programs are established for computation in proposed algorithms. These programs are not only used to compute effectively the numerical examples of this article but also to be applied for many different realistic problems.

Suggested Citation

  • Thao Nguyentrang & Tai Vovan, 2017. "Fuzzy clustering of probability density functions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 583-601, March.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:4:p:583-601
    DOI: 10.1080/02664763.2016.1177502
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    References listed on IDEAS

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    1. T. Pham-Gia & N. Turkkan & A. Bekker, 2006. "Bayesian Analysis in the L 1 -Norm of the Mixing Proportion Using Discriminant Analysis," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 64(1), pages 1-22, August.
    2. Wen-Liang Hung & Jenn-Hwai Yang, 2015. "Automatic clustering algorithm for fuzzy data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1503-1518, July.
    3. Tai Vo Van & T. Pham-Gia, 2010. "Clustering probability distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(11), pages 1891-1910.
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    Cited by:

    1. Tai Vovan & Dinh Phamtoan & Le Hoang Tuan & Thao Nguyentrang, 2021. "An automatic clustering for interval data using the genetic algorithm," Annals of Operations Research, Springer, vol. 303(1), pages 359-380, August.

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