K Means Clustering Report

K-means clustering is a powerful unsupervised machine learning algorithm that partitions data into k distinct clusters based on similarity. It iteratively assigns data points to centroids and recalculates these centroids until convergence is achieved. Widely used in market segmentation, image compression, document clustering, and anomaly detection, K-means is valued for its simplicity and computational efficiency. However, it requires careful selection of the number of clusters and initial centroids and is sensitive to outliers. Optimization techniques like the elbow method and K-means++ can enhance its performance, making it a crucial tool in data analysis.