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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