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A New Clustering Algorithm for Dynamic Data

  • Parisa RastinEmail author
  • Tong Zhang
  • Guénaël Cabanes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9949)

Abstract

In this paper, we propose an algorithm for the discovery and the monitoring of clusters in dynamic datasets. The proposed method is based on a Growing Neural Gas and learns simultaneously the prototypes and their segmentation using and estimation of the local density of data to detect the boundaries between clusters. The quality of our algorithm is evaluated on a set of artificial datasets presenting a set of static and dynamic cluster structures.

Keywords

Growing neural gas Clustering Density Dynamic data 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.LIPN UMR CNRS 7030, Université Paris 13 Sorbonne Paris CitéVilletaneuseFrance

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