Go beyond a surface-level understanding of KMeans. This guide provides a complete breakdown of the algorithm, starting with a practical look at tuning key Scikit-learn hyperparameters like n_clusters and init. We then dive into the crucial concepts of time and space complexity to understand how KMeans performs on large datasets. Finally, we explore the core mathematical objective, the challenges of finding an optimal solution, and how Lloyd's Algorithm works in practice.

Mini-Batch KMeans is a faster, memory-efficient version of KMeans, ideal for large datasets or streaming data. This guide explains how it works, its advantages, limitations, and when to use it.