K-Means

The K-Means algorithm can produce suboptimal clusters if the initial centroids are poorly chosen. This blog explains the importance of centroid initialization, demonstrates the problem with examples, and introduces KMeans++—a smarter approach that ensures well-separated centroids for faster and more reliable clustering.

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.

The Elbow Method and Silhouette Score are two powerful techniques for selecting the best number of clusters in K-Means. This guide explains WCSS, inertia, and how to evaluate cluster quality using cohesion and separation.

K-Means is a powerful unsupervised machine learning algorithm used to partition a dataset into a pre-determined number of distinct, non-overlapping clusters. It works by iteratively assigning data points to the nearest cluster "centroid" and then updating the centroid's position based on the mean of the assigned points. This guide breaks down the geometric intuition behind K-Means, explores its core assumptions and limitations, and introduces important variations you should k