Caesar AI Atlas

K-means Clustering

Also known as: K means Clustering

Caesar AI Atlas Definition

K-means clustering is an unsupervised learning method that groups examples into a specified number of clusters based on proximity to cluster centroids. It is widely used for segmentation, pattern discovery, and exploratory analysis, but requires choosing the number of clusters in advance.

Other Definitions

K-means Clustering Source

A method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster.

Concept Comparisons

Technical AlternativeMachine Learning

K-means Clustering vs Density-based Spatial Clustering of Applications With Noise

A side-by-side comparison of K-means Clustering and DBSCAN. Understand how centroid-based grouping differs from density-based clustering with noise detection.

Technical AlternativeMachine Learning

K-means Clustering vs Hierarchical Clustering

A side-by-side comparison of K-means Clustering and Hierarchical Clustering. Understand how flat centroid-based grouping differs from tree-like nested cluster structures.

Related Terms

ClusteringK-meansUnsupervised LearningCentroid-based ClusteringDivisive ClusteringHierarchical Clustering