Euro-Par 2018: Parallel Processing Workshops

Parallel and distributed solutions are essential for clustering data streams due to the large volumes of data. This paper first examines a direct adaptation of a recently developed prototype-based algorithm into three existing parallel frameworks. Based on the evaluation of performance, the paper then presents a customised pipeline framework that combines incremental and twophase learning into a balanced approach that dynamically allocates the available processing resources. This new framework is evaluated on a collection of synthetic datasets. The experimental results reveal that the framework not only produces correct final clusters on the one hand, but also significantly improves the clustering efficiency.