Abstract
Clustering is one of machine learning’s tasks when given objects must be split into specific groups based on distance between them. Its applications include different fields such as pattern matching, data compression and image analysis. Many programing languages allow to create clustering algorithms, though using already implemented ones is much easier. MATLAB includes a few of them. Knowing the performance of MATLAB’s cluster analysis algorithms may help choose the more optimal hardware for a given problem.
The work is partially supported by the Russian Foundation for Basic Research (project No. 19-07-00525 A – Developing flow-based models of routing problems in telecommunications networks).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Babichev, S., Lytvynenko, V., Taif, M.A.: Estimation of the inductive model of objects clustering stability based on the k-means algorithm for different levels of data noise. Radio Electron. Comput. Sci. Manage. 4(39), 54–60 (2016). https://doi.org/10.15588/1607-3274-2016-4-7
MacKay, D.: An example inference task: clustering. In: Information Theory, Inference and Learning Algorithms, pp. 284–292. Cambridge University Press, Cambridge (2003). ISBN 978-0-521-64298-9. MR 2012999
Choose Cluster Analysis Method. (n.d.) Retrieved 5/13/2020 from MATLAB & Simulink. https://www.mathworks.com/help/stats/choose-cluster-analysis-method.html
Nielsen, F.: Hierarchical clustering. Introduction to HPC with MPI for Data Science. Undergraduate Topics in Computer Science, pp. 195–211. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-21903-5_8
Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification (PDF). IEEE Trans. Inf. Theor. 13(1), 21–27 (1967). https://doi.org/10.1109/TIT.1967.1053964
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis, E., Han, J., Fayyad, U.M. (eds.) Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96). AAAI Press, pp. 226–231 (1996). ArXiv:10.1.1.121.9220. ISBN 1-57735-004-9
Perrone, V., Jenkins, P.A., Spano, D., Teh, Y.W.: Poisson Random Fields for Dynamic Feature Models (2016). arXiv:1611.07460
k-means clustering - MATLAB kmeans (n.d.) Retrieved 5/13/2020 from MathWorks Help Center (2020). https://www.mathworks.com/help/stats/kmeans.html
Find k-nearest neighbors using input data - MATLAB knnsearch (n.d.) Retrieved 5/13/2020 from MathWorks Help Center (2020). https://www.mathworks.com/help/stats/knnsearch.html
Pairwise distance between pairs of observations - MATLAB pdist (n.d.) Retrieved 5/13/2020 from MathWorks Help Center (2020). https://www.mathworks.com/help/stats/pdist.html
Measure the Performance of Your Code - MATLAB & Simulink (n.d.) Retrieved 5/13/2020 from MathWorks Help Center (2020). https://www.mathworks.com/help/matlab/matlab_prog/measure-performance-of-your-program.html
https://github.com/berkut126/MatlabPerformance/blob/master/NIPS.csv (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ivanov, A., Natalia, Z., Veronika, A. (2020). CPU vs GPU Performance of MATLAB Clustering Algorithms. In: Vishnevskiy, V.M., Samouylov, K.E., Kozyrev, D.V. (eds) Distributed Computer and Communication Networks: Control, Computation, Communications. DCCN 2020. Communications in Computer and Information Science, vol 1337. Springer, Cham. https://doi.org/10.1007/978-3-030-66242-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-66242-4_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66241-7
Online ISBN: 978-3-030-66242-4
eBook Packages: Computer ScienceComputer Science (R0)