Abstract
Clustering is one of the main data mining techniques used to analyze and group data, but often applications have to deal with a very large amount of spatially distributed data for which most of the clustering algorithms available so far are impractical. In this paper we present P2PRASTER, a distributed algorithm relying on a gossip–based protocol for clustering that exploits the RASTER algorithm and has been designed to handle big data in a decentralized manner. The experiments carried out show that P2PRASTER returns perfect results under both optimal and non-optimal conditions, and also provides excellent scalability.
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Mariani, A., Epicoco, I., Cafaro, M., Pulimeno, M. (2023). Grid-Based Contraction Clustering in a Peer-to-Peer Network. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13811. Springer, Cham. https://doi.org/10.1007/978-3-031-25891-6_28
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DOI: https://doi.org/10.1007/978-3-031-25891-6_28
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