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Evaluation of Clustering Algorithms on HPC Platforms

Author

Listed:
  • Juan M. Cebrian

    (Computer Engineering Department (DITEC), University of Murcia, 30100 Murcia, Spain)

  • Baldomero Imbernón

    (Computer Science Department, Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain)

  • Jesús Soto

    (Computer Science Department, Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain)

  • José M. Cecilia

    (Computer Engineering Department (DISCA), Universitat Politécnica de Valéncia (UPV), 46022 Valencia, Spain)

Abstract

Clustering algorithms are one of the most widely used kernels to generate knowledge from large datasets. These algorithms group a set of data elements (i.e., images, points, patterns, etc.) into clusters to identify patterns or common features of a sample. However, these algorithms are very computationally expensive as they often involve the computation of expensive fitness functions that must be evaluated for all points in the dataset. This computational cost is even higher for fuzzy methods, where each data point may belong to more than one cluster. In this paper, we evaluate different parallelisation strategies on different heterogeneous platforms for fuzzy clustering algorithms typically used in the state-of-the-art such as the Fuzzy C-means (FCM), the Gustafson–Kessel FCM (GK-FCM) and the Fuzzy Minimals (FM). The experimental evaluation includes performance and energy trade-offs. Our results show that depending on the computational pattern of each algorithm, their mathematical foundation and the amount of data to be processed, each algorithm performs better on a different platform.

Suggested Citation

  • Juan M. Cebrian & Baldomero Imbernón & Jesús Soto & José M. Cecilia, 2021. "Evaluation of Clustering Algorithms on HPC Platforms," Mathematics, MDPI, vol. 9(17), pages 1-20, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:17:p:2156-:d:628910
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    References listed on IDEAS

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    1. Ruiz de la Hermosa González-Carrato, Raúl, 2018. "Wind farm monitoring using Mahalanobis distance and fuzzy clustering," Renewable Energy, Elsevier, vol. 123(C), pages 526-540.
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