K-Means
An unsupervised clustering algorithm that partitions data into 'k' distinct groups by minimizing the distance between points and their cluster centers.
Think of It Like This
Like organizing a chaotic closet by throwing all clothes into three distinct piles based on which pile is physically closest.
The algorithm iteratively updates the centroid of each cluster and reassigns data points until convergence. It is highly efficient but sensitive to the initial placement of centroids and outliers. K-means assumes clusters are spherical and requires the user to predetermine the number of clusters 'k'.