When you are creating a Clustering Model, what common ML algorithm are you using?

Multiclass Logistic Regression is a supervised learning algorithm used for classification problems with more than two possible outcomes, not for clustering.

K-means is a popular unsupervised machine learning algorithm specifically designed for clustering, which aims to partition 'n' observations into 'k' clusters where each observation belongs to the cluster with the nearest mean (centroid).

Linear Regression is a supervised learning algorithm used for regression tasks, predicting a continuous output variable, not for grouping data into clusters.

Two-Class Neural Network is a supervised learning algorithm used for binary classification, distinguishing between two categories, not for clustering.

Decision Forest Regression is a supervised learning algorithm used for regression tasks to predict continuous values, not for grouping data into clusters.