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An automatic clustering for interval data using the genetic algorithm

Author

Listed:
  • Tai Vovan

    (Can Tho University)

  • Dinh Phamtoan

    (University of Science
    Vietnam National University
    VanLang University)

  • Le Hoang Tuan

    (Vietnam National University
    University of Information Technology)

  • Thao Nguyentrang

    (University of Science
    Vietnam National University)

Abstract

This paper proposes an Automatic Clustering algorithm for Interval data using the Genetic algorithm (ACIG). In this algorithm, the overlapped distance between intervals is applied to determining the suitable number of clusters. Moreover, to optimize in clustering, we modify the Davies & Bouldin index, and to improve the crossover, mutation, and selection operators of the original genetic algorithm. The convergence of ACIG is theoretically proved and illustrated by the numerical examples. ACIG can be implemented effectively by the established Matlab procedure. Through the experiments on data sets with different characteristics, the proposed algorithm has shown the outstanding advantages in comparison to the existing ones. Recognizing the images by the proposed algorithm gives the potential in real applications of this research.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:303:y:2021:i:1:d:10.1007_s10479-020-03606-8
    DOI: 10.1007/s10479-020-03606-8
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    References listed on IDEAS

    as
    1. Tai VoVan & Thao NguyenTrang, 2018. "Similar coefficient for cluster of probability density functions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(8), pages 1792-1811, April.
    2. Tai Vovan, 2017. "-distance and classification problem by Bayesian method," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(3), pages 385-401, February.
    3. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    4. 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.
    5. Tai VoVan & Thao Nguyen Trang, 2018. "Similar Coefficient of Cluster for Discrete Elements," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 19-36, May.
    6. Onur Şeref & Ya-Ju Fan & Elan Borenstein & Wanpracha A. Chaovalitwongse, 2018. "Information-theoretic feature selection with discrete $$k$$ k -median clustering," Annals of Operations Research, Springer, vol. 263(1), pages 93-118, April.
    7. 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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