Probabilistic and More General Uncertainty-Based (e.g., Fuzzy) Approaches to Crisp Clustering Explain the Empirical Success of the K-Sets Algorithm

• Main Authors: Vladik Kreinovich, Olga Kosheleva, Shahnaz N. Shahbazova, Songsak Sriboonchitta
• Format: Book Series
• Published: 2020
• Subjects: Computer Science; Mathematics
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85081610724&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/68347
• Institution: Chiang Mai University

© 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...