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ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
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Data Mining and Knowledge Discovery
Sparse on-line Gaussian processes
Neural Computation
A new approximate maximal margin classification algorithm
The Journal of Machine Learning Research
New supervision architecture based on on-line modelling of non-stationary data
Neural Computing and Applications
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Neural Computation
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
The huller: a simple and efficient online SVM
ECML'05 Proceedings of the 16th European conference on Machine Learning
An online kernel change detection algorithm
IEEE Transactions on Signal Processing - Part II
IEEE Transactions on Signal Processing
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy min-max neural networks -- Part 2: Clustering
IEEE Transactions on Fuzzy Systems
A new neural network for cluster-detection-and-labeling
IEEE Transactions on Neural Networks
An overview of statistical learning theory
IEEE Transactions on Neural Networks
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This paper presents a new online clustering algorithm called SAKM (Self-Adaptive Kernel Machine) which is developed to learn continuously evolving clusters from non-stationary data. Based on SVM and kernel methods, the SAKM algorithm uses a fast adaptive learning procedure to take into account variations over time. Dedicated to online clustering in a multi-class environment, the algorithm designs an unsupervised neural architecture with self-adaptive abilities. Based on a specific kernel-induced similarity measure, the SAKM learning procedures consist of four main stages: Creation, Adaptation, Fusion and Elimination. In addition to these properties, the SAKM algorithm is attractive to be computationally efficient in online learning of real-drifting targets. After a theoretical study of the error convergence bound of the SAKM local learning, a comparison with NORMA and ALMA algorithms is made. In the end, some experiments conducted on simulation data, UCI benchmarks and real data are given to illustrate the capacities of the SAKM algorithm for online clustering in non-stationary and multi-class environment.