Validating fuzzy partitions obtained through c-shells clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
On fuzzy cluster validity indices
Fuzzy Sets and Systems
GAPS: A clustering method using a new point symmetry-based distance measure
Pattern Recognition
A cluster validity index for fuzzy clustering
Information Sciences: an International Journal
Robust neural-fuzzy method for function approximation
Expert Systems with Applications: An International Journal
A swarm-inspired projection algorithm
Pattern Recognition
A time-domain-constrained fuzzy clustering method and its application to signal analysis
Fuzzy Sets and Systems
Clustering by competitive agglomeration
Pattern Recognition
An information-theoretic fuzzy C-spherical shells clustering algorithm
Fuzzy Sets and Systems
Mathematical and Computer Modelling: An International Journal
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images
Artificial Intelligence in Medicine
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Several generalizations of the fuzzy c-shells (FCS) algorithm are presented for characterizing and detecting clusters that are hyperellipsoidal shells. An earlier generalization, the adaptive fuzzy c-shells (AFCS) algorithm, is examined in detail and is found to have global convergence problems when the shapes to be detected are partial. New formulations are considered wherein the norm inducing matrix in the distance metric is unconstrained in contrast to the AFCS algorithm. The resulting algorithm, called the AFCS-U algorithm, performs better for partial shapes. Another formulation based on the second-order quadrics equation is considered. These algorithms can detect ellipses and circles in 2D data. They are compared with the Hough transform (HT)-based methods for ellipse detection. Existing HT-based methods for ellipse detection are evaluated, and a multistage method incorporating the good features of all the methods is used for comparison. Numerical examples of real image data show that the AFCS algorithm requires less memory than the HT-based methods, and it is at least an order of magnitude faster than the HT approach