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.
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