A ROBUST FUZZY CLASSIFICATION MAXIMUM LIKELIHOOD CLUSTERING FRAMEWORK

DOI10.1142/S0218488513500360zbMath1323.62069OpenAlexW1966496031MaRDI QIDQ3449273

Chih-Ying Lin, Min-Shen Yang, Yi-Cheng Tian

Publication date: 4 November 2015

Published in: International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0218488513500360

zbMATH Keywords

robustnessclusteringmodel-based clusteringfuzzy clusteringfuzzy \(c\)-means\(k\)-meansclassification maximum likelihood

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (1)

Detecting change-points for shifts in mean and variance using fuzzy classification maximum likelihood change-point algorithms

Cites Work

• An invisible hybrid color image system using spread vector quantization neural networks with penalized FCM
• Fuzzy classification maximum likelihood algorithms for mixed-Weibull distributions
• A survey of fuzzy clustering
• Trimmed \(k\)-means: An attempt to robustify quantizers
• Vector quantization in DCT domain using fuzzy possibilistic c-means based on penalized and compensated constraints
• Estimation of parameters in latent class models using fuzzy clustering algorithms
• Mean shift-based clustering
• Clustering Criteria and Multivariate Normal Mixtures
• The estimation of the gradient of a density function, with applications in pattern recognition
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Model-Based Gaussian and Non-Gaussian Clustering
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Fuzzy sets
• A new approach to clustering

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