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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2017: Advances in Knowledge Discovery and Data Mining pp 429–440Cite as

Parallel Visual Assessment of Cluster Tendency on GPU

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10235))

Abstract

Determining the number of clusters in a data set is a critical issue in cluster analysis. The Visual Assessment of (cluster) Tendency (VAT) algorithm is an effective tool for investigating cluster tendency, which produces an intuitive image of matrix as the representation of complex data sets. However, VAT can be computationally expensive for large data sets due to its \( O\left( {N^{2} } \right) \) time complexity. In this paper, we propose an efficient parallel scheme to accelerate the original VAT using NVIDIA GPU and CUDA architecture. We show that, on a range of data sets, the GPU-based VAT features good scalability and can achieve significant speedups compared to the original algorithm.

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Acknowledgment

This work was partially supported by the NVIDIA GPU Education Center awarded to Tsinghua University.

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Authors and Affiliations

  1. Intelligent Computing Lab, Division of Informatics, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, People’s Republic of China

    Tao Meng & Bo Yuan

Authors
  1. Tao Meng
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  2. Bo Yuan
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Corresponding author

Correspondence to Bo Yuan .

Editor information

Editors and Affiliations

  1. Kangwon National University, Chuncheon, Korea (Republic of)

    Jinho Kim

  2. Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. University of Technology Sydney, Sydney, New South Wales, Australia

    Longbing Cao

  4. KAIST, Daejeon, Korea (Republic of)

    Jae-Gil Lee

  5. University of New South Wales, Sydney, New South Wales, Australia

    Xuemin Lin

  6. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

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Meng, T., Yuan, B. (2017). Parallel Visual Assessment of Cluster Tendency on GPU. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10235. Springer, Cham. https://doi.org/10.1007/978-3-319-57529-2_34

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  • DOI: https://doi.org/10.1007/978-3-319-57529-2_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57528-5

  • Online ISBN: 978-3-319-57529-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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