In this paper we present an online recursive clustering algorithm based on incremental and decremental Support Vector Machine (SVM). Developed to learn evolving clusters from non-stationary data, it is able to achieve an efficient multi-class clustering in a non-stationary environment. With a new similarity measure and different procedures (Creation, Adaptation: incremental and decremental learning, Fusion and Elimination) this classifier can provide optimal updated models of data. © Springer-Verlag Berlin Heidelberg 2008.
CITATION STYLE
Boukharouba, K., & Lecoeuche, S. (2008). Online clustering of non-stationary data using incremental and decremental SVM. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5163 LNCS, pp. 336–345). https://doi.org/10.1007/978-3-540-87536-9_35
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