How We Made 100M Vector Indexing in 20 Minutes Possible on PostgreSQL
1. Introduction In the past few months, we’ve heard consistent feedback from users and partners: while our goal of providing a scalable, high-performa...
Summary
K-means clustering is an unsupervised machine learning algorithm that partitions data into K distinct clusters by assigning each point to the nearest cluster centroid and then updating centroids iteratively until convergence.
The method minimizes within-cluster variance and works efficiently for tasks such as customer segmentation, image compression, and anomaly detection. It requires specifying the number of clusters in advance and is sensitive to initial centroid placement and outliers.