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Parallel edge-based visual assessment of cluster tendency on GPU

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Abstract

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 datasets. The improved VAT (iVAT) incorporates a path-based distance metric into VAT to improve its effectiveness on complex-shaped datasets. The efficient formulation of the iVAT algorithm (efiVAT) further reduces the computational complexity of iVAT from \(O(N^3)\) to \(O(N^2)\). In this paper, we propose eVAT, an edge-based algorithm that can replicate the output of efiVAT but is more efficient and more suitable for parallelism. We also propose a parallel scheme to accelerate eVAT using NVIDIA GPU and CUDA architecture. We show that, on a range of datasets, the GPU-based eVAT features good scalability and can achieve significant speedups.

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Acknowledgements

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

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Authors and Affiliations

  1. Graduate School at Shenzhen, Tsinghua University, Tsinghua Campus, The University Town, 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.

Additional information

This paper is an extension Version of the PAKDD’2017 Long Presentation paper “Parallel Visual Assessment of Cluster Tendency on GPU” [20].

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Meng, T., Yuan, B. Parallel edge-based visual assessment of cluster tendency on GPU. Int J Data Sci Anal 6, 287–295 (2018). https://doi.org/10.1007/s41060-018-0100-7

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