MapReduce-Based Growing Neural Gas for Scalable Cluster Environments
| Autor: | Wolfgang Benn, Johannes Fliege |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Structure (mathematical logic)
Neural gas Artificial neural network Process (engineering) Computer science Distributed computing Topology (electrical circuits) 02 engineering and technology 010501 environmental sciences 01 natural sciences 020204 information systems Face (geometry) Scalability 0202 electrical engineering electronic engineering information engineering Cluster (physics) 0105 earth and related environmental sciences |
| Zdroj: | Machine Learning and Data Mining in Pattern Recognition ISBN: 9783319419190 MLDM |
| DOI: | 10.1007/978-3-319-41920-6_43 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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