Statistical analysis with missing data
Statistical analysis with missing data
Algorithms for clustering data
Algorithms for clustering data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Application of clustering to estimate missing data and improve data integrity
ICSE '76 Proceedings of the 2nd international conference on Software engineering
Decision-making processes in pattern recognition (ACM monograph series)
Decision-making processes in pattern recognition (ACM monograph series)
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy c-means clustering of incomplete data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Will the real iris data please stand up?
IEEE Transactions on Fuzzy Systems
Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm
Neural Processing Letters
Relational visual cluster validity (RVCV)
Pattern Recognition Letters
Iterative imputation algorithms for process modeling with incomplete data
Intelligent Data Analysis
A fuzzy c-means clustering algorithm based on nearest-neighbor intervals for incomplete data
Expert Systems with Applications: An International Journal
A robust missing value imputation method for noisy data
Applied Intelligence
Clustering with proximity knowledge and relational knowledge
Pattern Recognition
A comparative study on TIBA imputation methods in FCMdd-based linear clustering with relational data
Advances in Fuzzy Systems - Special issue on Fuzzy Functions, Relations, and Fuzzy Transforms: Theoretical Aspects and Applications to Fuzzy Systems
Information Sciences: an International Journal
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An approach for clustering on the basis of incomplete dissimilarity data is given. The data is first completed using simple triangle inequality-based approximation schemes and then clustered using the non-Euclidean relational fuzzy c-means algorithm. Results of numerical tests are included.