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An automatic clustering for interval data using the genetic algorithm

  • S.I.: Data Mining and Decision Analytics
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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.

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Acknowledgements

For Le Hoang Tuan, this research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under Grant Number C2018-26-05.

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Authors and Affiliations

  1. College of Natural Science, Can Tho University, Can Tho, Vietnam

    Tai Vovan

  2. University of Science, Ho Chi Minh City, Vietnam

    Dinh Phamtoan & Thao Nguyentrang

  3. Vietnam National University, Ho Chi Minh City, Vietnam

    Dinh Phamtoan, Le Hoang Tuan & Thao Nguyentrang

  4. Faculty of Engineering, VanLang University, Ho Chi Minh City, Vietnam

    Dinh Phamtoan

  5. Department of Mathematics, University of Information Technology, Ho Chi Minh City, Vietnam

    Le Hoang Tuan

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  1. Tai Vovan
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  2. Dinh Phamtoan
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  3. Le Hoang Tuan
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  4. Thao Nguyentrang
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Correspondence to Thao Nguyentrang.

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Vovan, T., Phamtoan, D., Tuan, L.H. et al. An automatic clustering for interval data using the genetic algorithm. Ann Oper Res 303, 359–380 (2021). https://doi.org/10.1007/s10479-020-03606-8

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  • DOI: https://doi.org/10.1007/s10479-020-03606-8

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