Learning spatially variant dissimilarity (SVaD) measures
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning spatially variant dissimilarity (SVaD) measures
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Improving fuzzy c-means clustering based on feature-weight learning
Pattern Recognition Letters
A cluster validity index for fuzzy clustering
Pattern Recognition Letters
Non-Euclidean c-means clustering algorithms
Intelligent Data Analysis
New modifications and applications of fuzzy C-means methodology
Computational Statistics & Data Analysis
Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study
Fuzzy Sets and Systems
A new approach to clustering data with arbitrary shapes
Pattern Recognition
Clustering of time series data-a survey
Pattern Recognition
Unsupervised clustering algorithm based on normalized Mahalanobis distances
ACACOS'10 Proceedings of the 9th WSEAS international conference on Applied computer and applied computational science
Fuzzy clustering of human motor motion
Applied Soft Computing
Alternative fuzzy clustering algorithms with l1-norm and covariance matrix
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
DBCAMM: A novel density based clustering algorithm via using the Mahalanobis metric
Applied Soft Computing
Algorithm for fuzzy clustering of mixed data with numeric and categorical attributes
ICDCIT'05 Proceedings of the Second international conference on Distributed Computing and Internet Technology
Dynamic rough clustering and its applications
Applied Soft Computing
Strong fuzzy c-means in medical image data analysis
Journal of Systems and Software
Fuzzy c-means improvement using relaxed constraints support vector machines
Applied Soft Computing
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In this letter, we show that the Gustafson-Kessel (G-K) algorithm (1979) and the original adaptive fuzzy clustering (AFC) algorithm can be thought of as special cases of a more general algorithm. Our analysis shows that the G-K algorithm is better suited for ellipsoidal clusters of equal volume, whereas the original AFC algorithm is better suited for linear clusters. We also discuss a new variation of these algorithms, which can be used to improve the results of the G-K and AFC algorithms in some cases