K-means clustering as unsupervised learning

K-means clustering is a technique used in unsupervised learning, allowing data points to be classified into K groups without any guidance. Unlike supervised learning where training examples are provided, unsupervised learning aims to find patterns or associations in the data without prior knowledge of how the groups should be formed. K-means clustering is popular for its simplicity and scalability, making it useful in various domains such as customer segmentation in businesses.