What is the name of the unsupervised algorithm that partitions data into K clusters?

The unsupervised algorithm that partitions data into K clusters is known as K-means. This algorithm works by identifying K centroids in the data and assigning each data point to the closest centroid, which effectively groups the data into K clusters. The process involves iteratively updating the positions of the centroids based on the mean of the data points assigned to each cluster until the centroids stabilize. This method is widely used for clustering due to its simplicity and efficiency in handling large datasets.

In contrast, other algorithms mentioned perform different functions. For instance, K-nearest neighbors is a supervised learning algorithm used for classification and regression, where it makes predictions based on the closest labeled data points. Support vector machines focus primarily on classification tasks by finding the optimal separating hyperplane between different classes, while linear regression is a supervised technique used to model the relationship between a dependent variable and one or more independent variables, specifically for regression purposes. These characteristics distinctively separate K-means as the algorithm designed specifically for partitioning data into clusters.