Extreme physical information and objective function in fuzzy clustering

Fuzzy Sets and Systems

Volumn 128, Issue 3, 2002, Pages 285-303

  Ménard, Michel ^a   Eboueya, Michel ^a  

^a UNIVERSITÉ DE LA ROCHELLE   (France)

Author keywords

Classification; Extreme physical information; Fuzzy clustering; Measures of information

Indexed keywords

ALGORITHMS; CONSTRAINT THEORY; DATA REDUCTION; ENTROPY; INFORMATION ANALYSIS; MATHEMATICAL MODELS; MATRIX ALGEBRA; OPTIMIZATION;

EXTREME PHYSICAL INFORMATION (EPI); FUZZY CLUSTERING;

FUZZY SETS;

EID: 0037118558     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-0114(01)00071-9     Document Type: Article

References (27)

1. Weighted possibilistic c-means clustering algorithms
2. Validity-guided (re) clustering with applications to image segmentation
3. Selecting input variables for fuzzy models
4. Fuzzy model identification based on cluster estimation
5. Robust clustering methods: A unified view
6. Fisher information as a measure of time
7. Switching regression models and fuzzy clustering
8. Optimization of clustering criteria by reformulation
9. Gaussian mixture pdf approximation and fuzzy c-means clustering with entropy regularization
10. The possibilistic c-means algorithm: Insights and recommendations
11. A possibilistic approach to clustering
12. A maximum entropy approach to fuzzy clustering
13. Fuzzy clustering and switching regression models using ambiguity and distance rejects
14. The fuzzy c+2-means: Solving the ambiguity reject in clustering
15. An overview and new methods in fuzzy clustering
16. Fuzzy c-means as a regularization and maximum entropy approach
17. A deterministic annealing approach to clustering
18. A new approach to clustering
19. Approximate clustering via the mountain method
20. A survey of fuzzy clustering