Implementing the K-Means Clustering Algorithm

Implementation of K-Means Clustering Algorithm: This algorithm partitions n objects into K clusters based on maximizing intra-cluster similarity while minimizing inter-cluster similarity. Limitations include potentially uneven cluster sizes and sensitivity to noisy data. Enhanced approach: k-medoids method selects representative objects (medoids) instead of centroids to define clusters. Implementation steps: 1) Randomly initialize K medoids; 2) Assign remaining objects to nearest medoids; 3) Iteratively optimize medoid selection by minimizing replacement cost. Code implementation typically involves distance calculations, cluster assignment loops, and convergence checks.