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

DOI10.1007/978-3-030-38893-5_4zbMATH Open1484.68249OpenAlexW2691676973MaRDI QIDQ5015710

Shahnaz N. Shahbazova, Songsak Sriboonchitta, Olga Kosheleva, Vladik Kreinovich

Publication date: 9 December 2021

Published in: Recent Developments in Fuzzy Logic and Fuzzy Sets (Search for Journal in Brave)

Full work available at URL: https://digitalcommons.utep.edu/cgi/viewcontent.cgi?article=2101&context=cs_techrep

zbMATH Keywords

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Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Cites Work

• Strict Archimedean \(t\)-norms and \(t\)-conorms as universal approximators
• Probability Theory
• A First Course in Fuzzy Logic
• Fuzzy sets
• Title not available (Why is that?)
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