Which of the following algorithms is commonly used in Machine Learning for clustering tasks?       

K-Means is a popular clustering algorithm in machine learning that groups data points into 'k' clusters based on their similarities. The algorithm works by initializing 'k' cluster centroids, assigning each data point to the nearest centroid, and iteratively updating the centroids until the clusters are optimized. K-Means is commonly used in tasks such as market segmentation, image compression, and pattern recognition. Unlike classification algorithms like K-Nearest Neighbors or Logistic Regression, K-Means does not require labeled data.