The correct answer is B. K-means clustering.
Unsupervised learning is a type of machine learning where the algorithm does not have labeled data to learn from. Instead, it learns from unlabeled data by finding patterns and relationships in the data. Clustering is a type of unsupervised learning where the algorithm groups data points together based on their similarity. K-means clustering is a popular clustering algorithm that works by dividing data points into k clusters, where k is a number specified by the user. The algorithm starts by randomly selecting k data points as the cluster centers. Then, each data point is assigned to the cluster with the closest cluster center. The cluster centers are then updated based on the data points in each cluster. This process is repeated until the cluster centers no longer change.
Linear regression is a supervised learning algorithm that is used to predict a continuous value. It works by finding a linear relationship between the input data and the output data. Decision trees are a supervised learning algorithm that is used to classify data. They work by splitting the data into smaller and smaller subsets until each subset contains only data points of the same class. Logistic regression is a supervised learning algorithm that is used to predict a binary value. It works by finding a linear relationship between the input data and the output data, and then using that relationship to calculate the probability of the output being 1.
In conclusion, the correct answer is B. K-means clustering.