Exclusive Clustering

Exclusive clustering, also known as hard clustering, is a type of clustering where each data point can only belong to one cluster. This means there's no overlap between clusters; a data point is definitively assigned to a single group. The goal is to partition the data into distinct, non-overlapping clusters based on similarity. For example, K-Means is an exclusive clustering algorithm. It aims to partition n data points into k clusters in which each data point belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster.

Visit the following resources to learn more:

• @article@K-means | scikit-learn
• @article@Unsupervised Clustering: A Guide