Abstract
One of the well known density-based clustering algorithm is DBSCAN, which is commonly used for class identification in spatial databases. In this paper we propose its modification which could shorten the global computational time by introducing a simple data preprocessing with graphics processing unit (GPU). The GPU consists of many small cores which allow parallel computation using single instruction multiple data model (SIMD). Combining it with CPU power of computing system can improve application performance. In order to estimate the computation gain of our proposition compared with the original one we carried out a set of experiments on an artificial dataset.
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Cal, P., Woźniak, M. (2013). Data Preprocessing with GPU for DBSCAN Algorithm. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds) Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, vol 226. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00969-8_78
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DOI: https://doi.org/10.1007/978-3-319-00969-8_78
Publisher Name: Springer, Heidelberg
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