Interval type-2 relative entropy fuzzy C-means clustering

DOI10.1016/j.ins.2014.02.066zbMath1341.68178OpenAlexW2072199060MaRDI QIDQ726345

M. H. Fazel Zarandi, I. Burhan Türksen, Marzieh Zarinbal

Publication date: 8 July 2016

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2014.02.066

zbMATH Keywords

relative entropyinterval arithmeticfuzzy c-means clusteringinterval type-2 fuzzy set theoryinterval type-2 relative entropy fuzzy C-means clustering

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (10)

The likelihood ranking methods for interval type-2 fuzzy sets considering risk preferences ⋮ Unnamed Item ⋮ GT2-CFC: general type-2 collaborative fuzzy clustering method ⋮ Type-2 fuzzy cross-entropy and entropy measures and their applications ⋮ Type-2 fuzzy implications and fuzzy-valued approximation reasoning ⋮ Notes on type-2 triangular norms and their residual operators ⋮ Multi-central general type-2 fuzzy clustering approach for pattern recognitions ⋮ SMKFC-ER: semi-supervised multiple kernel fuzzy clustering based on entropy and relative entropy ⋮ Deterministic annealing Gustafson-Kessel fuzzy clustering algorithm ⋮ An Adaptive Self-Reduction Type-2 Fuzzy Clustering Algorithm for Pattern Recognition

Cites Work

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• On the Lambert \(w\) function
• Upper and lower values for the level of fuzziness in FCM
• Uncertainty measures for interval type-2 fuzzy sets
• Introduction to Interval Analysis
• Knowledge‐Based Clustering
• Numerical evaluation of the Lambert W function and application to generation of generalized Gaussian noise with exponent 1/2

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