Abstract
Growing Neural Gas (GNG) constitutes a neural network algorithm to create topology preserving representations of data, thus, being applicable in cluster analysis. With fast growing amounts of data, cluster analysis tasks face distributed data sets managed by cluster environments requiring scalable, parallel computation methods. In this paper we present a MapReduce-based version of the GNG training method. The algorithm is able to process large data sets on scalable cluster systems. We discuss its complexity and consider communication costs that arise from its structure. We conduct experiments on artificial data in different cluster environments to evaluate the algorithms scalability. Finally, we show that the algorithm is applicable for cluster analysis of large data sets in scalable cluster systems.
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Fliege, J., Benn, W. (2016). MapReduce-Based Growing Neural Gas for Scalable Cluster Environments. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_43
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DOI: https://doi.org/10.1007/978-3-319-41920-6_43
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