K-Means Clustering

What is K-Means Clustering?

K-Means is an unsupervised algorithm that partitions data into 'k' clusters by iteratively assigning points to the nearest centroid and updating the centroid.

Where did the term "K-Means Clustering" come from?

Proposed by Hugo Steinhaus (1956) and Stuart Lloyd (1957).

How is "K-Means Clustering" used today?

Used for customer segmentation and image compression.

Related Terms

• Machine Learning
• Distance Metrics