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Interval Fuzzy c-Regression Models with Competitive Agglomeration for Symbolic Interval-Valued Data

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Abstract

In this study, a novel approach, interval fuzzy c-regression models with competitive agglomeration (IFCRMCA), is proposed to deal with the symbolic interval-valued data. The proposed IFCRMCA approach can identify the partition of the interval-valued data using both the distances to the cluster centers and the errors of interval regression models for each cluster. Due to the concepts of competitive agglomeration is used in the proposed approach, the pre-determination of the cluster number in the proposed IFCRMCA is not necessary. Various real experiments are carried on and the experimentally results shows that the proposed approaches are superior to the existing approaches.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Ilan University, 1, Sec. 1, Shen-Lung Rd., I-Lan City, Taiwan

    Chen-Chia Chuang, Wei-Yang Lin & Chin-Wang Tao

  2. Department of Computer Science and Information Engineering, National Formosa University, 64, Wen-Hua Road, Huwei Jen, Yunlin County, Huwei, Taiwan

    Jin-Tsong Jeng

  3. Department of Information Technology, Kao Yuan University, 1821, Jhong-Shan Rd., Lu-Jhu Dist., Kaohsiung, Taiwan

    Chih-Ching Hsiao

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  1. Chen-Chia Chuang
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  2. Jin-Tsong Jeng
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  3. Wei-Yang Lin
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  4. Chih-Ching Hsiao
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  5. Chin-Wang Tao
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Correspondence to Jin-Tsong Jeng.

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Chuang, CC., Jeng, JT., Lin, WY. et al. Interval Fuzzy c-Regression Models with Competitive Agglomeration for Symbolic Interval-Valued Data. Int. J. Fuzzy Syst. 22, 891–900 (2020). https://doi.org/10.1007/s40815-020-00816-x

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  • DOI: https://doi.org/10.1007/s40815-020-00816-x

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