Convergence theory for fuzzy c-means: counterexamples and repairs
IEEE Transactions on Systems, Man and Cybernetics
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
Approximate clustering in very large relational data: Research Articles
International Journal of Intelligent Systems
Extending fuzzy and probabilistic clustering to very large data sets
Computational Statistics & Data Analysis
Complexity reduction for "large image" processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Reducing the time complexity of the fuzzy c-means algorithm
IEEE Transactions on Fuzzy Systems
Fast accurate fuzzy clustering through data reduction
IEEE Transactions on Fuzzy Systems
A contribution to convergence theory of fuzzy c-means and derivatives
IEEE Transactions on Fuzzy Systems
A New Convergence Proof of Fuzzy c-Means
IEEE Transactions on Fuzzy Systems
A novel possibilistic fuzzy leader clustering algorithm
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
Synthesis and characterization of gold nanoparticles: a fuzzy mathematical approach
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Partitive clustering (K-means family)
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Strong fuzzy c-means in medical image data analysis
Journal of Systems and Software
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In this short paper, a unified framework for performing density-weighted fuzzy c-means (FCM) clustering of feature and relational datasets is presented. The proposed approach consists of reducing the original dataset to a smaller one, assigning each selected datum a weight reflecting the number of nearby data, clustering the weighted reduced dataset using a weighted version of the feature or relational data FCM algorithm, and if desired, extending the reduced data results back to the original dataset. Several methods are given for each of the tasks of data subset selection, weight assignment, and extension of the weighted clustering results. The newly proposed weighted version of the non-Euclidean relational FCM algorithm is proved to produce the identical results as its feature data analog for a certain type of relational data. Artificial and real data examples are used to demonstrate and contrast various instances of this general approach.