The correct answer is C. K-means clustering.
K-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
K-means clustering is a popular choice for clustering because it is simple to understand and implement, and it can be used to cluster data points into a variety of different shapes and sizes.
Linear regression is a statistical method that is used to model the relationship between two or more variables. In the context of clustering, linear regression could be used to model the relationship between the features of a data point and the cluster that it belongs to. However, linear regression is not a clustering algorithm, and it is not typically used for clustering data points.
Support vector machines (SVMs) are a type of supervised machine learning algorithm that can be used for classification or regression tasks. SVMs are not typically used for clustering data points.
Principal component analysis (PCA) is a dimensionality reduction technique that is used to reduce the number of features in a data set. PCA is not typically used for clustering data points.