Which clustering algorithm is commonly used to group data by similarity?

K-Means is a widely used clustering algorithm designed to group data into clusters based on similarity. It operates by partitioning the data into a predefined number of clusters, denoted as 'K'. The algorithm assigns each data point to the nearest cluster center (or centroid) and then updates the centroids based on the mean of the points in each cluster. This iterative process continues until the assignments no longer change significantly, indicating that the clusters have stabilized.

The strength of K-Means lies in its simplicity and efficiency, making it suitable for large datasets. It works well when the clusters are spherical and of similar sizes, as it minimizes the variance within each cluster. The algorithm is effective for various applications, such as market segmentation, image compression, and anomaly detection, where identifying groups of similar items is critical.

Other options like Linear Regression, Support Vector Machines, and Random Forest are primarily used for predictive modeling and classification tasks rather than clustering. Linear Regression focuses on modeling the relationship between dependent and independent variables, Support Vector Machines are typically used for classification by finding hyperplanes, and Random Forest is an ensemble technique for classification and regression that does not inherently group data by similarity. These methods serve different purposes in the realm of data analysis and do not provide the clustering