title: A new Growing Neural Gas for clustering data streams.
abstract: Clustering data streams is becoming the most efficient way to cluster a massive dataset. This task requires a process capable of partitioning observations continuously with restrictions of memory and time. In this paper we present a new algorithm, called G-Stream, for clustering data streams by making one pass over the data. G-Stream is based on growing neural gas, that allows us to discover clusters of arbitrary shapes without any assumptions on the number of clusters. By using a reservoir, and applying a fading function, the quality of clustering is improved. The performance of the proposed algorithm is evaluated on public datasets.
authors.affiliation: University of Paris 13, Sorbonne Paris City LIPN-UMR 7030 - CNRS, 99, av. J-B Clément-F-93430 Villetaneuse, France. Electronic address: Mohammed.Ghesmoune@lipn.univ-paris13.fr.
authors.forename: Mohammed
authors.initials: M
authors.lastname: Ghesmoune
authors.affiliation: University of Paris 13, Sorbonne Paris City LIPN-UMR 7030 - CNRS, 99, av. J-B Clément-F-93430 Villetaneuse, France. Electronic address: Mustapha.Lebbah@lipn.univ-paris13.fr.
authors.forename: Mustapha
authors.initials: M
authors.lastname: Lebbah
authors.affiliation: University of Paris 13, Sorbonne Paris City LIPN-UMR 7030 - CNRS, 99, av. J-B Clément-F-93430 Villetaneuse, France. Electronic address: Hanene.Azzag@lipn.univ-paris13.fr.
authors.forename: Hanene
authors.initials: H
authors.lastname: Azzag
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D016208:Databases, Factual / Q000639:trends; D066264:Datasets as Topic / Q000639:trends*; D006801:Humans; D008568:Memory; D016571:Neural Networks, Computer; D013223:Statistics as Topic / Q000639:trends
keywords: Data stream clustering; GNG; Topological structure