Algorithms for clustering data
Algorithms for clustering data
The nature of statistical learning theory
The nature of statistical learning theory
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering incomplete relational data using the non-Euclidean relational fuzzy c-means algorithm
Pattern Recognition Letters
Fuzzy c-means clustering of incomplete data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
A kernel-based subtractive clustering method
Pattern Recognition Letters
Scalable visual assessment of cluster tendency for large data sets
Pattern Recognition
Kernel based automatic clustering using modified particle swarm optimization algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Robust fuzzy relational classifier incorporating the soft class labels
Pattern Recognition Letters
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
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
A kernelized fuzzy c-means algorithm for automatic magnetic resonance image segmentation
Journal of Computational Methods in Sciences and Engineering
Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study
Fuzzy Sets and Systems
Profile based cross-document coreference using kernelized fuzzy relational clustering
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm
Information Sciences: an International Journal
TSK fuzzy model using kernel-based fuzzy c-means clustering
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Non-negative matrix factorization on Kernels
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Extended Gaussian kernel version of fuzzy c-means in the problem of data analyzing
Expert Systems with Applications: An International Journal
Fuzzy C-means based clustering for linearly and nonlinearly separable data
Pattern Recognition
A SVDD approach of fuzzy classification for analog circuit fault diagnosis with FWT as preprocessor
Expert Systems with Applications: An International Journal
A kernel prototype-based clustering algorithm
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Improved Fuzzy Clustering Algorithms in Segmentation of DC-enhanced breast MRI
Journal of Medical Systems
DBCAMM: A novel density based clustering algorithm via using the Mahalanobis metric
Applied Soft Computing
Fuzzy data mining: a literature survey and classification framework
International Journal of Networking and Virtual Organisations
Smoothing approach to alleviate the meager rating problem in collaborative recommender systems
Future Generation Computer Systems
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Robust kernelized approach to clustering by incorporating new distance measure
Engineering Applications of Artificial Intelligence
Triangular kernel nearest-neighbor-based clustering algorithm for discovering true clusters
PAKDD'12 Proceedings of the 2012 Pacific-Asia conference on Emerging Trends in Knowledge Discovery and Data Mining
Consensus strategy for clustering using RC-images
Pattern Recognition
Extended fuzzy c-means: an analyzing data clustering problems
Cluster Computing
Improving project-profit prediction using a two-stage forecasting system
Computers and Industrial Engineering
Semi-supervised clustering of large data sets with kernel methods
Pattern Recognition Letters
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
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There is a trend in recent machine learning community to construct a nonlinear version of a linear algorithm using the 'kernel method', e.g. Support Vector Machines (SVMs), kernel principal component analysis, kernel fisher discriminant analysis and the recent kernel clustering algorithms. In unsupervised clustering algorithms using kernel method, typically, a nonlinear mapping is used first to map the data into a potentially much higher feature space, where clustering is then performed. A drawback of these kernel clustering algorithms is thatthe clustering prototypes lie in high dimensional feature space and hence lack clear and intuitive descriptions unless using additional projection approximation from the feature to the data space as done in the existing literatures. In this paper, a novel clustering algorithm using the 'kernel method' based on the classical fuzzy clustering algorithm (FCM) is proposed and called as kernel fuzzy c-means algorithm (KFCM). KFCM adopts a new kernel-induced metric in the data space to replace the original Euclidean norm metric in FCM and the clustered prototypes still lie in the data space so that the clustering results can be reformulated and interpreted in the original space. Our analysis shows that KFCM is robust to noise and outliers and also tolerates unequal sized clusters. And finally this property is utilized to cluster incomplete data. Experiments on two artificial and one real datasets show that KFCM hasbetter clustering performance and more robust than several modifications of FCM for incomplete data clustering.