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
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies
The Journal of Machine Learning Research
Robust fuzzy relational classifier incorporating the soft class labels
Pattern Recognition Letters
Recognition of semiconductor defect patterns using spatial filtering and spectral clustering
Expert Systems with Applications: An International Journal
A Kernel-Based Two-Stage One-Class Support Vector Machines Algorithm
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Outlier identification and market segmentation using kernel-based clustering techniques
Expert Systems with Applications: An International Journal
Graph nodes clustering with the sigmoid commute-time kernel: A comparative study
Data & Knowledge Engineering
Benchmarking graph-based clustering algorithms
Image and Vision Computing
Semi-supervised fuzzy clustering: A kernel-based approach
Knowledge-Based Systems
Adaptive Image Watermarking Approach Based on Kernel Clustering and HVS
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Iterative Bayesian fuzzy clustering toward flexible icon-based assistive software for the disabled
Information Sciences: an International Journal
A new method for the initialization of clustering algorithms based on histogram analysis
VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study
Fuzzy Sets and Systems
Graph nodes clustering based on the commute-time kernel
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Fuzzy C-means based clustering for linearly and nonlinearly separable data
Pattern Recognition
A kernel prototype-based clustering algorithm
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Approximate kernel k-means: solution to large scale kernel clustering
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Robust kernel fuzzy clustering
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
DBCAMM: A novel density based clustering algorithm via using the Mahalanobis metric
Applied Soft Computing
Objective function-based clustering
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Clustering interval data through kernel-induced feature space
Journal of Intelligent Information Systems
Audio watermarking scheme robust against desynchronization attacks based on kernel clustering
Multimedia Tools and Applications
Speeding-up the kernel k-means clustering method: A prototype based hybrid approach
Pattern Recognition Letters
Kernel fuzzy c-means with automatic variable weighting
Fuzzy Sets and Systems
Theoretical aspects of mapping to multidimensional optimal regions as a multi-classifier
Intelligent Data Analysis
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By using a kernel function, data that are not easily separable in the original space can be clustered into homogeneous groups in the implicitly transformed high-dimensional feature space. Kernel k-means algorithms have recently been shown to perform better than conventional k-means algorithms in unsupervised classification. However, few reports have examined the benefits of using a kernel function and the relative merits of the various kernel clustering algorithms with regard to the data distribution. In this study, we reformulated four representative clustering algorithms based on a kernel function and evaluated their performances for various data sets. The results indicate that each kernel clustering algorithm gives markedly better performance than its conventional counterpart for almost all data sets. Of the kernel clustering algorithms studied in the present work, the kernel average linkage algorithm gives the most accurate clustering results.