K-Means Clustering: How Unsupervised Learning Groups Unlabeled Data

K-Means clustering is an unsupervised machine learning algorithm that automatically groups similar data points without requiring labeled training data. It works by placing a user-defined number of centroids (K) in the feature space, assigning each data point to its nearest centroid, then repeatedly repositioning centroids to the average of their assigned points until the clusters stabilize. The algorithm minimizes Within-Cluster Sum of Squares (WCSS) to produce compact, well-separated groupings. Choosing the right value of K is a key challenge, typically addressed using the Elbow Method or Silhouette Score. Common real-world applications include customer segmentation, product recommendation, and identifying patterns in transactional data.