A new Growing Neural Gas for clustering data streams

作者：Ghesmoune Mohammed^*; Lebbah Mustapha; Azzag Hanene

来源：Neural Networks, 2016, 78: 36-50.

DOI：10.1016/j.neunet.2016.02.003

• Data stream clustering
• Topological structure
• GNG

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.

• 出版日期2016-6