K-Means
K-Means is an unsupervised clustering algorithm that partitions data into k predefined clusters. Each data point is assigned to the cluster with the nearest centroid, and the algorithm iteratively updates centroid positions to minimize within-cluster variance.
Background
K-Means is one of the most widely used clustering techniques in data science. It was popularized in the 1960s and remains essential in exploratory data analysis. The iterative process continues until the centroids stabilize or a convergence criterion is met.
Applications
- Customer segmentation in marketing.
- Image compression by reducing color palettes.
- Anomaly detection, identifying unusual groups of points.
Strengths and challenges
- ✅ Fast and efficient on large datasets.
- ✅ Easy to implement and interpret.
- ❌ Sensitive to outliers and initialization.
- ❌ Assumes spherical clusters of similar size.
- ❌ Requires predefining k.
K-Means is often considered the entry point to clustering: simple enough to understand intuitively, yet powerful enough to reveal meaningful groupings in data. The algorithm works like a negotiation between points and centroids—points seek the closest centroid, and centroids adjust to the average of their assigned points, repeating until stability is reached.
Despite its elegance, K-Means has well-known limitations. It struggles with non-spherical clusters or data with varying densities, where algorithms like DBSCAN or Gaussian Mixture Models are more appropriate. Moreover, because it requires the number of clusters k upfront, practitioners often rely on techniques like the elbow method or the silhouette score to estimate a reasonable value.
Still, K-Means remains highly popular due to its scalability and efficiency. It is frequently used in text mining to group documents, in image compression to simplify color palettes, and in recommendation systems to cluster users with similar behaviors.
📚 Further Reading
- MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations.
