Enhancing k-Means Algorithm with Tensor Processing Unit

Pranava Mummoju; Anna Wolff; Martin Perdacher; Claudia Plant; Christian Böhm

doi:10.1109/BigData55660.2022.10020427

Enhancing k-Means Algorithm with Tensor Processing Unit

Pranava Mummoju, Anna Wolff, Martin Perdacher, Claudia Plant, Christian Böhm

Publications: Contribution to book › Contribution to proceedings › Peer Reviewed

Abstract

Clustering in Data Mining is the process of discovering groups of similar objects in data. The k-Means clustering algorithm, is designed to partition data into k distinct groups or clusters. With recent growth in data production, the need to scale up existing algorithms and computational ability has increased. Google introduced the Tensor Processing Unit (TPU), a powerful hardware, to meet the growing computational needs of modern technologies. In this paper, we aim to enhance the k-Means algorithm with the use of the Google TPU in terms of runtime while preserving the quality of the clustering results. We developed two versions that distribute training on the TPU in two different ways. The clustering results of the versions have advantages that complement each other in terms of runtime and accuracy.

Original language	English
Title of host publication	2022 IEEE International Conference on Big Data (IEEE BigData)
Editors	Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Place of Publication	Piscataway
Publisher	IEEE
Pages	194-200
Number of pages	7
ISBN (Electronic)	978-1-6654-8045-1
DOIs	https://doi.org/10.1109/BigData55660.2022.10020427
Publication status	Published - 1 Dec 2022

Austrian Fields of Science 2012

102033 Data mining

Keywords

Clustering
Data Mining
k-mean
Tensor Processing Unit (TPU)

Access to Document

10.1109/BigData55660.2022.10020427

http://eprints.cs.univie.ac.at/7494/

Cite this

Mummoju, P., Wolff, A., Perdacher, M., Plant, C., & Böhm, C. (2022). Enhancing k-Means Algorithm with Tensor Processing Unit. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), 2022 IEEE International Conference on Big Data (IEEE BigData) (pp. 194-200). IEEE. https://doi.org/10.1109/BigData55660.2022.10020427

Mummoju, Pranava ; Wolff, Anna ; Perdacher, Martin et al. / Enhancing k-Means Algorithm with Tensor Processing Unit. 2022 IEEE International Conference on Big Data (IEEE BigData). editor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. Piscataway : IEEE, 2022. pp. 194-200

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title = "Enhancing k-Means Algorithm with Tensor Processing Unit",

abstract = "Clustering in Data Mining is the process of discovering groups of similar objects in data. The k-Means clustering algorithm, is designed to partition data into k distinct groups or clusters. With recent growth in data production, the need to scale up existing algorithms and computational ability has increased. Google introduced the Tensor Processing Unit (TPU), a powerful hardware, to meet the growing computational needs of modern technologies. In this paper, we aim to enhance the k-Means algorithm with the use of the Google TPU in terms of runtime while preserving the quality of the clustering results. We developed two versions that distribute training on the TPU in two different ways. The clustering results of the versions have advantages that complement each other in terms of runtime and accuracy.",

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year = "2022",

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doi = "10.1109/BigData55660.2022.10020427",

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Mummoju, P, Wolff, A, Perdacher, M, Plant, C & Böhm, C 2022, Enhancing k-Means Algorithm with Tensor Processing Unit. in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), 2022 IEEE International Conference on Big Data (IEEE BigData). IEEE, Piscataway, pp. 194-200. https://doi.org/10.1109/BigData55660.2022.10020427

Enhancing k-Means Algorithm with Tensor Processing Unit. / Mummoju, Pranava; Wolff, Anna; Perdacher, Martin et al.
2022 IEEE International Conference on Big Data (IEEE BigData). ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. Piscataway: IEEE, 2022. p. 194-200.

Publications: Contribution to book › Contribution to proceedings › Peer Reviewed

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AU - Mummoju, Pranava

AU - Wolff, Anna

AU - Perdacher, Martin

AU - Plant, Claudia

AU - Böhm, Christian

PY - 2022/12/1

Y1 - 2022/12/1

N2 - Clustering in Data Mining is the process of discovering groups of similar objects in data. The k-Means clustering algorithm, is designed to partition data into k distinct groups or clusters. With recent growth in data production, the need to scale up existing algorithms and computational ability has increased. Google introduced the Tensor Processing Unit (TPU), a powerful hardware, to meet the growing computational needs of modern technologies. In this paper, we aim to enhance the k-Means algorithm with the use of the Google TPU in terms of runtime while preserving the quality of the clustering results. We developed two versions that distribute training on the TPU in two different ways. The clustering results of the versions have advantages that complement each other in terms of runtime and accuracy.

AB - Clustering in Data Mining is the process of discovering groups of similar objects in data. The k-Means clustering algorithm, is designed to partition data into k distinct groups or clusters. With recent growth in data production, the need to scale up existing algorithms and computational ability has increased. Google introduced the Tensor Processing Unit (TPU), a powerful hardware, to meet the growing computational needs of modern technologies. In this paper, we aim to enhance the k-Means algorithm with the use of the Google TPU in terms of runtime while preserving the quality of the clustering results. We developed two versions that distribute training on the TPU in two different ways. The clustering results of the versions have advantages that complement each other in terms of runtime and accuracy.

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ER -

Enhancing k-Means Algorithm with Tensor Processing Unit

Abstract

Austrian Fields of Science 2012

Keywords

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