Description: K-Center Clustering is a clustering method within unsupervised learning that focuses on minimizing the maximum distance between data points and their assigned cluster center. Unlike other clustering algorithms, such as K-Means, which aim to minimize the average distance, K-Center focuses on the farthest point within each cluster, making it particularly useful in situations where ensuring that all points are as close as possible to their center is critical, even in the worst-case scenario. This approach is especially relevant in various applications where uniformity and proximity are crucial, such as network planning, resource distribution, and facility location. The algorithm begins by selecting K initial centers and then assigns each point to the nearest center. Subsequently, the cluster centers are updated based on the current assignments, repeating the process until no significant changes occur. This methodology allows for more robust classification compared to other methods, especially in datasets with noise or outliers. In summary, K-Center Clustering is a valuable technique in the field of unsupervised learning, providing an effective way to group data in a manner that minimizes maximum distances, resulting in more compact and coherent clusters.
