Abstract
Recently, a new empirically successful algorithm was proposed for crisp clustering: the K-sets algorithm. In this paper, we show that a natural uncertainty-based formalization of what is clustering automatically leads to the mathematical ideas and definitions behind this algorithm. Thus, we provide an explanation for this algorithm’s empirical success.
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Acknowledgements
This work was supported in part by the National Science Foundation grants HRD-0734825 and HRD-1242122 (Cyber-ShARE Center of Excellence) and DUE-0926721, and by an award “UTEP and Prudential Actuarial Science Academy and Pipeline Initiative” from Prudential Foundation.
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Kreinovich, V., Kosheleva, O., Shahbazova, S.N., Sriboonchitta, S. (2020). Probabilistic and More General Uncertainty-Based (e.g., Fuzzy) Approaches to Crisp Clustering Explain the Empirical Success of the K-Sets Algorithm. In: Shahbazova, S., Sugeno, M., Kacprzyk, J. (eds) Recent Developments in Fuzzy Logic and Fuzzy Sets. Studies in Fuzziness and Soft Computing, vol 391. Springer, Cham. https://doi.org/10.1007/978-3-030-38893-5_4
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DOI: https://doi.org/10.1007/978-3-030-38893-5_4
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