ACM Computing Surveys (CSUR)
Entropy-based fuzzy clustering and fuzzy modeling
Fuzzy Sets and Systems
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy C-Means Clustering Algorithm Based on Kernel Method
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm
Neural Processing Letters
An investigation of mountain method clustering for large data sets
Pattern Recognition
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Validity-guided (re)clustering with applications to image segmentation
IEEE Transactions on Fuzzy Systems
A fuzzy k-modes algorithm for clustering categorical data
IEEE Transactions on Fuzzy Systems
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Kernel based automatic clustering using modified particle swarm optimization algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Automatic kernel clustering with a Multi-Elitist Particle Swarm Optimization Algorithm
Pattern Recognition Letters
Kernelized fuzzy attribute C-means clustering algorithm
Fuzzy Sets and Systems
Outlier identification and market segmentation using kernel-based clustering techniques
Expert Systems with Applications: An International Journal
Clustering: A neural network approach
Neural Networks
Approach to image segmentation based on interval type-2 fuzzy subtractive clustering
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Applying a novel decision rule to the semi-supervised clustering method based on one-class SVM
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
The novel seeding-based semi-supervised fuzzy clustering algorithm inspired by diffusion processes
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Kernel fuzzy c-means with automatic variable weighting
Fuzzy Sets and Systems
International Journal of Hybrid Intelligent Systems
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In this paper the conventional subtractive clustering method is extended by calculating the mountain value of each data point based on a kernel-induced distance instead of the conventional sum-of-squares distance. The kernel function is a generalization of the distance metric that measures the distance between two data points as the data points are mapped into a high dimensional space. Use of the kernel function makes it possible to cluster data that is linearly non-separable in the original space into homogeneous groups in the transformed high dimensional space. Application of the conventional subtractive method and the kernel-based subtractive method to well-known data sets showed the superiority of the proposed approach.