Constructing a fuzzy controller from data
Fuzzy Sets and Systems - Special issue on methods for data analysis in classificatin and control
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
AI Game Programming Wisdom
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy C-Means Clustering Algorithm Based on Kernel Method
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
The Journal of Machine Learning Research
Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm
Neural Processing Letters
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Entropy-based criterion in categorical clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attribute weighted mercer kernel based fuzzy clustering algorithm for general non-spherical datasets
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A survey of kernel and spectral methods for clustering
Pattern Recognition
A note on the Gustafson-Kessel and adaptive fuzzy clustering algorithms
IEEE Transactions on Fuzzy Systems
A new kernel-based fuzzy clustering approach: support vector clustering with cell growing
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
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
Fuzzy clustering based on generalized entropy and its application to image segmentation
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Clustering with proximity knowledge and relational knowledge
Pattern Recognition
DBCAMM: A novel density based clustering algorithm via using the Mahalanobis metric
Applied Soft Computing
Signatures: Definitions, operators and applications to fuzzy modelling
Fuzzy Sets and Systems
A novel fuzzy clustering algorithm with between-cluster information for categorical data
Fuzzy Sets and Systems
Fast window fusion using fuzzy equivalence relation
Pattern Recognition Letters
Conjecturable knowledge discovery: A fuzzy clustering approach
Fuzzy Sets and Systems
Kernel fuzzy c-means with automatic variable weighting
Fuzzy Sets and Systems
FHC: The fuzzy hyper-prototype clustering algorithm
International Journal of Knowledge-based and Intelligent Engineering Systems - Intelligent Information Processing: Techniques and Applications
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In this study, we present a comprehensive comparative analysis of kernel-based fuzzy clustering and fuzzy clustering. Kernel based clustering has emerged as an interesting and quite visible alternative in fuzzy clustering, however, the effectiveness of this extension vis-a-vis some generic methods of fuzzy clustering has neither been discussed in a complete manner nor the performance of clustering quantified through a convincing comparative analysis. Our focal objective is to understand the performance gains and the importance of parameter selection for kernelized fuzzy clustering. Generic Fuzzy C-Means (FCM) and Gustafson-Kessel (GK) FCM are compared with two typical generalizations of kernel-based fuzzy clustering: one with prototypes located in the feature space (KFCM-F) and the other where the prototypes are distributed in the kernel space (KFCM-K). Both generalizations are studied when dealing with the Gaussian kernel while KFCM-K is also studied with the polynomial kernel. Two criteria are used in evaluating the performance of the clustering method and the resulting clusters, namely classification rate and reconstruction error. Through carefully selected experiments involving synthetic and Machine Learning repository (http://archive.ics.uci.edu/beta/) data sets, we demonstrate that the kernel-based FCM algorithms produce a marginal improvement over standard FCM and GK for most of the analyzed data sets. It has been observed that the kernel-based FCM algorithms are in a number of cases highly sensitive to the selection of specific values of the kernel parameters.