Mathematics
Cluster Analysis and K-Means Clustering
Quick fact
In k-means clustering, the algorithm's name reflects its core: it partitions n data points into k clusters, each defined by a centroid, and it works by minimizing the total distance between points and their cluster centroid—a process that is guaranteed to converge but not necessarily to the best solution.
Why this is interesting
Have you ever wondered how Netflix groups movies or how scientists classify galaxies without prior labels? In the world of data, we often need to find natural groups in a sea of numbers—this is the magic of cluster analysis.