Abstract
Nowadays, when the data size grows exponentially, it becomes more and more difficult to extract useful information in reasonable time. One very important technique to exploit data is clustering and many algorithms have been proposed like k-means and its variations (k-medians, kernel k-means etc.), DBSCAN, OPTICS and others. The time complexity of all these methods is prohibitive (NP hard) in order to make decisions on time and the solution is either new faster algorithms to be invented, or increase the performance of the old well tested ones. Distributed, parallel, and multi-core GPU computing or even combination of these platforms consist a very promising method to speed up clustering techniques. In this paper, parallel versions of the above mentioned algorithms were used and implemented in order to increase their performance and consequently, their perspectives in several fields like industry, political/social sciences, telecommunications businesses, and intrusion detection in big networks. The parallel versions of clustering techniques are presented here and two different cases of their applications on different fields are illustrated. The results obtained are very promising concerning their quality and performance and therefore, the perspective of using clustering techniques in industry and sciences is increased.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Emani, C.K., Cullot, N., Nicolle, C.: Understandable big data: a survey. Comput. Sci. Rev. 17, 70–81 (2015). https://doi.org/10.1016/j.cosrev.2015.05.002
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1: Statistics, pp. 281–297. University of California Press, Berkeley (1967). https://projecteuclid.org/euclid.bsmsp/1200512992
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density based algorithm for discovering clusters in large spatial databases with noise. In: KDD-96 Proceedings, pp. 226–231 (1996). https://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf
MPICH: High-Performance Portable Message Passing Interface (2018). https://www.mpich.org/
OpenMP: The OpenMP API Specification for Parallel Programming (2018). https://www.openmp.org/
CUDA Zone: NVDIA Accelerated Computing (2018). https://developer.nvidia.com/cuda-zone
Zou, H., Zou, Z., Wang, X.: An enhanced K-means algorithm for water quality analysis of the Haihe River in China. Int. J. Environ. Res. Public Health 12(11), 14400–14413 (2015). https://doi.org/10.3390/ijerph121114400
Dubey, S.R., Dixit, P., Singh, N., Gupta, J.P.: Infected fruit part detection using k-means clustering segmentation technique international. J. Artif. Intell. Interact. Multimed. 2(2), 65–72. https://doi.org/10.9781/ijimai.2013.229
NallamReddy, S., Behera, S., Karadagi, S., Desik, A.: Application of multiple random centroid (MRC) based k-means clustering algorithm in insurance-a review article. Oper. Res. Appl. Int. J. 1(1), 15–21 (2014)
Ghorbani, A., Farzai, S.: Fraud detection in automobile insurance using a data mining based approach. Int. J. Mechatron. Electr. Comput. Technol. 8(27), 3764–3771 (2018). https://doi.org/IJMEC/10.225163
Momeni, M., Mohseni, M., Soofi, M.: Clustering stock market companies via k-means algorithm. Kuwait Chapter Arab. J. Bus. Manag. Rev. 4(5), 1–10 (2015). https://doi.org/10.12816/0018959
Zhao, J., Zhang, W., Liu, Y.: Improved k-means cluster algorithm in telecommunications enterprises customer segmentation. In: 2010 Information IEEE International Conference on Theory and Information Security (ICITIS), Beijing, pp. 167–169 (2010). https://doi.org/10.1109/ICITIS.2010.5688749
Savvas, I.K., Tselios, D., Garani, G.: Distributed and multi-core version of k-means algorithm. Int. J. Grid Util. Comput. (2018, accepted). http://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijguc
Savvas, I.K., Tselios, D.: Combining distributed and multi-core programming techniques to increase the performance of k-means algorithm. In: 26th IEEE International WETICE Conference, pp. 96–100 (2017)
Savvas, I.K., Sofianidou, G.N.: A novel near-parallel version of k-means algorithm for n-dimensional data objects using MPI. Int. J. Grid Util. Comput. 7(2), 80–91 (2016)
Savvas, I.K., Sofianidou, G.N.: Parallelizing k-means algorithm for 1-d data using MPI. In: 2014 IEEE 23rd International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), Milano, pp. 179–184 (2016). https://doi.org/10.1109/wetice.2014.13
Savvas, I.K., Sofianidou, G.N., Kechadi, M.: Applying the k-means algorithm in big raw data sets with Hadoop and MapReduce. In: Big Data Management, Technologies, and Applications, pp. 23–46. IGI Global (2014). https://doi.org/10.4018/978-1-4666-4699-5, ISBN13: 9781466646995, ISBN10: 1466646993
Savvas, I.K., Kechadi, M.: Mining on the cloud: k-means with MapReduce. In: 2nd International Conference on Cloud Computing and Services Science, CLOSER, pp. 413–418 (2012)
Savvas, I.K., Tselios, D.: Parallelizing DBSCAN algorithm using MPI. In: 2016 IEEE 25th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), Paris, pp. 77–82 (2016). https://doi.org/10.1109/wetice.2016.26
Ye, L., Qiuru, C., Haixu, X., Guangping, Z.: Customer segmentation for telecom with the k-means clustering method. Inf. Technol. J. 12, 409–413 (2013)
Savvas, I.K., Chaikalis, C., Messina, F., Tselios, D.: Understanding customers’ behaviour of telecommunication companies increasing the efficiency of clustering techniques. In: 25th IEEE Telecommunications Forum TELFOR, Serbia (2017)
Mazis, I.T.: Dissertationes academicae geopoliticae. Papazisis Publications, Athens (2015)
World Bank: Countries and Economies, January 2015. http://data.worldbank.org/country
Savvas, I.K., Stogiannos, A., Mizis, I.T.: A study of comparative clustering of EU-countries using the DBSCAN and k-means techniques within the theoretical framework of systemic geopolitical analysis. Int. J. Grid Util. Comput. 8(2), 94–108 (2017)
Jolliffe, I.T.: Principal Component Analysis, Series: Springer Series in Statistics, 2nd edn., XXIX, 487, p. 28 illus. Springer, New York (2002). ISBN 978-0-387-95442-4
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Savvas, I.K., Garani, G. (2019). Perspectives of Fast Clustering Techniques. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18). IITI'18 2018. Advances in Intelligent Systems and Computing, vol 875. Springer, Cham. https://doi.org/10.1007/978-3-030-01821-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-01821-4_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01820-7
Online ISBN: 978-3-030-01821-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)