Abstract
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.
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Boukharouba, K., Lecoeuche, S. (2008). Online Clustering of Non-stationary Data Using Incremental and Decremental SVM. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_35
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DOI: https://doi.org/10.1007/978-3-540-87536-9_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87535-2
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