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IPFCM Clustering Algorithm Under Euclidean and Hausdorff Distance Measure for Symbolic Interval Data

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Abstract

In this paper, a novel interval possibilistic fuzzy c-means (IPFCM) clustering method is proposed for clustering symbolic interval data. Clustering algorithms are important methods that required in pattern recognition, data mining and text mining, etc. Most of cluster algorithms focus on single-valued data. The interval fuzzy c-means (IFCM) clustering method is first proposed for symbolic interval data. However, the IFCM may not have good performance when facing noisy date or data with outliers. Hence, the advantages of the proposed IPFCM clustering algorithm in this study mainly overcome the noisy date or data with outliers in symbolic interval data. In the proposed approach, two different distances; namely, Euclidean and Hausdorff distance measures are all individually considered. That is, based on Lagrange multipliers nonlinear programming method, the proposed IPFCM clustering algorithm under Euclidean and Hausdorff distance measure was individually derived for symbolic interval data. From our experimental results, the proposed IPFCM clustering algorithm indeed has better performance than the IFCM and the interval fuzzy possibilistic c-means (IFPCM) clustering algorithm under different distance measures for the noisy date or data with outlier problem. Besides, the proposed IPFCM clustering algorithm is also implemented on windows smart phone platform and shown better performance as expected. Finally, a real data set is added for testing.

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Acknowledgements

This work was supported by National Science Council under Grant NSC 101-2221-E-150-061-MY3.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Formosa University, 64, Wen-Hua Rd., Huwei Jen, 632, Yunlin County, Taiwan

    Jin-Tsong Jeng & Sheng-Chieh Chang

  2. Department of Finance & Economics, Fujian Business University, 19, Huang Pu Rd., Gulou District, Fuzhou, Fujian, China

    Chih-Ming Chen

  3. Department of Electrical Engineering, National Ilan University, 1, Sec. 1 Shen-Lung Rd., Yilan, I-Lan County, 260, Taiwan

    Chen-Chia Chuang

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  1. Jin-Tsong Jeng
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  2. Chih-Ming Chen
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  3. Sheng-Chieh Chang
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  4. Chen-Chia Chuang
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Correspondence to Chen-Chia Chuang.

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Jeng, JT., Chen, CM., Chang, SC. et al. IPFCM Clustering Algorithm Under Euclidean and Hausdorff Distance Measure for Symbolic Interval Data. Int. J. Fuzzy Syst. 21, 2102–2119 (2019). https://doi.org/10.1007/s40815-019-00707-w

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  • DOI: https://doi.org/10.1007/s40815-019-00707-w

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