Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6863209 | Neural Networks | 2016 | 15 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Mohammed Ghesmoune, Mustapha Lebbah, Hanene Azzag,