Online Clustering of Non-stationary Data Using Incremental and Decremental SVM (original) (raw)

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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Authors and Affiliations

  1. LAGIS UMR 8146, Université des Sciences et Technologies de Lille, Bâtiment P2, 59655, Villeneuve d’Ascq, France
    Khaled Boukharouba & Stéphane Lecoeuche
  2. Ecole des Mines de Douai - Département Informatique et Automatique, , 941, Rue Charles Bourseul, BP838, 59508, Douai, France
    Khaled Boukharouba & Stéphane Lecoeuche

Authors

  1. Khaled Boukharouba
  2. Stéphane Lecoeuche

Editor information

Véra Kůrková Roman Neruda Jan Koutník

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© 2008 Springer-Verlag Berlin Heidelberg

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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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