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Performance of the K-means and fuzzy C-means algorithms in big data analytics

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Abstract

Nowadays, cloud computing is used by most organizations to utilize cloud resources and services in dealing with big data. Besides, machine learning techniques are applied to extract the most valuable information from raw data provided by different resources. This paper evaluates the performance of the K-means (KM) and fuzzy C-means (FCM) algorithms in terms of the clustering time and accuracy. Data clustering has been applied to encrypted data as an instance of data analytics in both distributed and centralized-based environments. Furthermore, two different datasets are used in the designed framework, one which contains well-separated data and the other with overlapped data to check the performance of the two clustering algorithms for both data types. Moreover, adding different numbers of virtual machines in a distributed environment can obviously speed up the clustering time and reduce computational overheads. Experiments show that with overlapped data, FCM can obtain better accuracy than KM, whereas, in the case of well-separated data, the KM algorithm performs better than FCM with higher accuracy and less clustering time.

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Data availability

The used datasets have been provided from the Kaggle website https://www.kaggle.com/.

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Authors and Affiliations

  1. College of Information Technology, University of Bahrain, Zallaq, Sukhir, 32038, Southern Governate, Kingdom of Bahrain

    Zainab Salman & Alauddin Alomary

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  1. Zainab Salman
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  2. Alauddin Alomary
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Correspondence to Zainab Salman.

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Salman, Z., Alomary, A. Performance of the K-means and fuzzy C-means algorithms in big data analytics. Int. j. inf. tecnol. 16, 465–470 (2024). https://doi.org/10.1007/s41870-023-01436-y

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  • DOI: https://doi.org/10.1007/s41870-023-01436-y

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