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A Survey and Systematic Categorization of Parallel K-Means and Fuzzy-C-Means Algorithms

Ahmed A. M. Jamel1,∗, Bahriye Akay2,†

1 Erciyes University, Institute of Natural and Applied Sciences, Department of Computer Engineering, 38039, Melikgazi, Kayseri, Turkey
2 Erciyes University, Engineering Faculty, Department of Computer Engineering, 38039, Melikgazi, Kayseri, Turkey

* Corresponding Authors: Email:

Computer Systems Science and Engineering 2019, 34(5), 259-281.


Parallel processing has turned into one of the emerging fields of machine learning due to providing consistent work by performing several tasks simultaneously, enhancing reliability (the presence of more than one device ensures the workflow even if some devices disrupted), saving processing time and introducing low cost and high-performance computation units. This research study presents a survey of parallel K-means and Fuzzy-c-means clustering algorithms based on their implementations in parallel environments such as Hadoop, MapReduce, Graphical Processing Units, and multi-core systems. Additionally, the enhancement in parallel clustering algorithms is investigated as hybrid approaches in which K-means and Fuzzy-c-means clustering algorithms are integrated with metaheuristic and other traditional algorithms.


Cite This Article

A. A. M. Jamel and B. Akay, "A survey and systematic categorization of parallel k-means and fuzzy-c-means algorithms," Computer Systems Science and Engineering, vol. 34, no.5, pp. 259–281, 2019.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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