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Neural Computation
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ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
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This paper presents a kernel-based clustering algorithm called SAKM (Self-Adaptive Kernel Machine) that is developed to learn continuously evolving clusters from non-stationary data. Dedicated to online clustering in multi-class environment, this algorithm is based on an unsupervised learning process with self-adaptive abilities. This process is achieved through three main stages: clusters creation (with an initialization procedure), online clusters adaptation and clusters fusion. Thanks to a new specific kernel-induced similarity measure, the SAKM algorithm is attractive to be very computationally efficient in online applications. At the end, some experiments illustrate the capacities of our algorithm in non-stationary environment.