What is K-Means Clustering?
K-Means Clustering — An unsupervised ML algorithm that groups data into K distinct non-overlapping subsets or clusters.
K-Means groups data points into K clusters by iteratively assigning each point to the nearest cluster center and recalculating centers. It is fast, simple, and widely used for customer segmentation, document grouping, and anomaly detection in structured data.
Frequently Asked Questions
How do I choose the right number of clusters (K)?
Use the elbow method — plot model fit against K values and look for the bend where adding more clusters stops improving results significantly. Silhouette analysis is another common method.
When does K-Means work poorly?
When clusters have non-spherical shapes, very different sizes, or overlapping boundaries. DBSCAN and Gaussian Mixture Models handle these cases better.
Is K-Means used in modern AI?
Yes. It is used for data preprocessing, feature engineering, customer segmentation, and as a component in larger systems. Its speed and simplicity make it a practical workhorse algorithm.