K-Means Clustering MCQ Test
15 Questions
Time: 25 mins
Beginner-Intermediate
K-Means Clustering MCQ Test
Test your knowledge of K-Means clustering, including algorithm steps, objective function, limitations and good practices.
Easy: 5 Q
Medium: 6 Q
Hard: 4 Q
K-Means Clustering: MCQ Practice
K-Means is a popular unsupervised algorithm for partitioning data into K clusters. These questions focus on how the algorithm works, how to choose K and when K-Means is (and is not) a good choice.
Cluster Around Centroids
K-Means assigns points to the nearest centroid and then updates centroids as the mean of assigned points.
K-Means Workflow
Initialize Centroids → Assign Points to Nearest → Recompute Centroids → Repeat Until Convergence