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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Clustering Over Data Streams Based on Growing Neural Gas
verfasst von : Mohammed Ghesmoune, Mustapha Lebbah, Hanene Azzag
Erschienen in: Advances in Knowledge Discovery and Data Mining
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Clustering data streams 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 shape 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 data sets.