Online Clustering of Non-stationary Data Using Incremental and Decremental SVM

Authors:
Khaled Boukharouba;Stéphane Lecoeuche
Affiliations:
LAGIS UMR 8146, Université des Sciences et Technologies de Lille, Villeneuve d'Ascq, France 59655 and Ecole des Mines de Douai - Département Informatique et Automatique, , Douai, France ...;LAGIS UMR 8146, Université des Sciences et Technologies de Lille, Villeneuve d'Ascq, France 59655 and Ecole des Mines de Douai - Département Informatique et Automatique, , Douai, France ...
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Year:
2008

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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.