AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

K-means clustering is characterized by its reliance on strictly defined cluster boundaries, which is foundational to the algorithm's functionality. In k-means, the process begins by partitioning data into a predetermined number of clusters, denoted as "k." Each cluster is represented by its centroid, which is the mean location of all data points assigned to that cluster.

During the algorithm's iterative process, each data point is assigned to the nearest centroid based on a distance metric, typically Euclidean distance. This assignment is what establishes the strict boundaries of clusters, as each point can belong to only one cluster based on proximity. The centroids are then recalculated based on the new assignments, and the process repeats until the clusters stabilize.

This clear demarcation of clusters underlines the algorithm's effectiveness in forming distinct groups in structured datasets but also highlights its limitation when dealing with datasets with overlapping clusters. Unlike some other clustering methods that account for more fluid and probabilistic boundaries, k-means creates well-defined, non-overlapping clusters, emphasizing its characteristic focus on strict boundary enforcement.