K-Means Clustering

K-Means Clustering is an unsupervised learning algorithm used to partition a dataset into K distinct clusters based on feature similarity. The algorithm aims to minimize the variance within each cluster while maximizing the distance between clusters.

The algorithm works in the following steps:

1. Initialization: Randomly select K centroids.
2. Assignment: Assign each data point to the nearest centroid.
3. Update: Recalculate the centroids based on current assignments.
4. Repeat: Continue until convergence.

K-Means is widely used for customer segmentation, image compression, and anomaly detection.