Technical GlossaryMachine Learning

K-Means

One of the most common clustering algorithms, which partitions data points into k clusters based on distance to centroids.

K-Means is one of the most widely used clustering algorithms in unsupervised learning. Its goal is to partition data points into a predefined number of clusters according to their similarity. Because it is fast and easy to understand, it is commonly used for customer segmentation, behavioral analysis, and exploratory studies. However, it has limitations such as requiring the number of clusters in advance, sensitivity to outliers, and no guarantee of a global optimum.