Micro-Batching growing neural gas for clustering data streams using Spark Streaming

Abstract

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.