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.


Clustering k-means DBSCAN Parallel computing 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringT.E.I. of ThessalyLarissaGreece

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