In recent years, the data stream clustering problem has gained considerable attention in the literature. Clustering data streams requires a process capable of partitioning observations continuously while taking into account restrictions of memory and time. In this paper we present MBG-Stream, a Micro-Batching version of the growing neural gas approach, aimed to clustering data streams by making one pass over the data. MBG-Stream allows us to discover clusters of arbitrary shapes without any assumptions on the number of clusters. The proposed algorithm is implemented on a "distributed" streaming platform, the Spark Streaming API, and its performance is evaluated on public data sets.
Ghesmoune, M., Lebbah, M., & Azzag, H. (2015). Micro-Batching growing neural gas for clustering data streams using Spark Streaming. In Procedia Computer Science (Vol. 53, pp. 158–166). Elsevier B.V. https://doi.org/10.1016/j.procs.2015.07.290