K-means clustering is a popular unsupervised classification algorithm employed in several domains, e.g., imaging, segmentation, or compression. Nevertheless, the number of clusters k, fixed apriori, affects mainly the clustering quality. Current State-of-the-art k-means implementations could automatically set of the number of clusters. However, they result in unreasonable processing time while classifying large volumes of data. In this paper, we propose a novel solution based on kd-tree to determine the number of cluster k in the context of massive data for preprocessing data science projects or in near-real-time applications. We demonstrate how our solution outperforms current solutions in terms of clustering quality, and processing time on massive data.