A new Growing Neural Gas for clustering data streams. (June 2016)

• Record Type: Journal Article
• Authors: Ghesmoune, Mohammed; Lebbah, Mustapha; Azzag, Hanene
• Is Part Of: Neural networks. Volume 78(2016)
• Page Start: 36
• Page End: 50
• Publication Date: 2016-06
• Subjects: Data stream clustering -- Topological structure -- GNG; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2016.02.003
• Languages: English
• ISSNs: 0893-6080

Abstract

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.