Elements of information theory
Elements of information theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Kernel Principle Component Analysis in Pixels Clustering
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Information theory and statistics: a tutorial
Communications and Information Theory
Clustering with Bregman Divergences
The Journal of Machine Learning Research
A survey of kernel and spectral methods for clustering
Pattern Recognition
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modified global k-means algorithm for minimum sum-of-squares clustering problems
Pattern Recognition
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Engineering Applications of Artificial Intelligence
Greedy unsupervised multiple kernel learning
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
An online kernel-based clustering approach for value function approximation
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Speeding-up the kernel k-means clustering method: A prototype based hybrid approach
Pattern Recognition Letters
Kernel clustering using a hybrid memetic algorithm
Natural Computing: an international journal
A hybrid memetic algorithm for global optimization
Neurocomputing
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Kernel k-means is an extension of the standard k-means clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage, through a global search procedure consisting of several executions of kernel k-means from suitable initializations. This algorithm does not depend on cluster initialization, identifies nonlinearly separable clusters, and, due to its incremental nature and search procedure, locates near-optimal solutions avoiding poor local minima. Furthermore, two modifications are developed to reduce the computational cost that do not significantly affect the solution quality. The proposed methods are extended to handle weighted data points, which enables their application to graph partitioning. We experiment with several data sets and the proposed approach compares favorably to kernel k-means with random restarts.