Among clustering algorithms, K-means is a widely popular and effective method. It partitions data into K distinct clusters, where K is a pre-defined number.

The goal of K-means is to minimize distances within clusters and maximize distances between clusters. This creates internally similar and externally distinct groups. K-means has numerous applications, such as:

• Customer segmentation: grouping customers for targeted marketing;

• Document clustering: organizing documents by topic;

• Image segmentation: dividing images for object recognition;

• Anomaly detection: identifying unusual data points.