Abstract
Since the notion of hesitant fuzzy set was introduced, some clustering algorithms have been proposed to cluster hesitant fuzzy data. Beside of hesitation in data, there is some hesitation in the clustering (classification) of a crisp data set. This hesitation may be arise in the selection process of a suitable clustering (classification) algorithm and initial parametrization of a clustering (classification) algorithm. Hesitant fuzzy set theory is a suitable tool to deal with this kind of problems. In this study, we introduce two different points of view to apply hesitant fuzzy sets in the data mining tasks, specially in the clustering algorithms.
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Acknowledgements
The first author gratefully acknowledges the support of the Ministry of Science, Research and Technology of the Islamic Republic of Iran and Shahid Bahonar University of Kerman.
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Aliahmadipour, L., Torra, V., Eslami, E. (2017). On Hesitant Fuzzy Clustering and Clustering of Hesitant Fuzzy Data. In: Torra, V., Dahlbom, A., Narukawa, Y. (eds) Fuzzy Sets, Rough Sets, Multisets and Clustering. Studies in Computational Intelligence, vol 671. Springer, Cham. https://doi.org/10.1007/978-3-319-47557-8_10
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DOI: https://doi.org/10.1007/978-3-319-47557-8_10
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