Abstract
We are living in a world of heavy data bombing and the term Big Data is a key issue these days. The variety of applications, where huge amounts of data are produced (can be expressed in PBs and more), is great in many areas such as: Biology, Medicine, Astronomy, Geology, Geography, to name just a few. This trend is steadily increasing. Data Mining is the process for extracting useful information from large data-sets. There are different approaches to discovering properties of datasets. Machine Learning is one of them. In Machine Learning, unsupervised learning deals with unlabeled datasets. One of the primary approaches to unsupervised learning is clustering which is the process of grouping similar entities together. Therefore, it is a challenge to improve the performance of such techniques, especially when we are dealing with huge amounts of data. In this work, we present a survey of techniques which increase the efficiency of two well-known clustering algorithms, k-means and DBSCAN.
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Acknowledgments
The reported study was funded by RFBR according to the research project 19-01-246-a, 19-07-00329-a, 18-01-00402-a, 18-08-00549-a.
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Savvas, I.K., Michos, C., Chernov, A., Butakova, M. (2020). High Performance Clustering Techniques: A Survey. In: Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19). IITI 2019. Advances in Intelligent Systems and Computing, vol 1156. Springer, Cham. https://doi.org/10.1007/978-3-030-50097-9_26
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