Probabilistic and More General Uncertainty-Based (e.g., Fuzzy) Approaches to Crisp Clustering Explain the Empirical Success of the K-Sets Algorithm
© 2020, Springer Nature Switzerland AG. 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...
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| Main Authors: | , , , |
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| Format: | Book Series |
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2020
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| Subjects: | |
| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85081610724&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/68347 |
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| Institution: | Chiang Mai University |
| Summary: | © 2020, Springer Nature Switzerland AG. 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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