The correct answer is: C. k-means clustering.
K-means clustering is an unsupervised machine learning algorithm that groups data points into clusters. The algorithm works by first randomly selecting k data points as cluster centers. Then, each data point is assigned to the cluster with the closest cluster center. The cluster centers are then recomputed as the mean of the data points in each cluster. This process is repeated until the cluster centers no longer change.
Agglomerative clustering is a hierarchical clustering algorithm that starts with each data point in its own cluster. Then, the algorithm repeatedly merges the two closest clusters until there is only one cluster left.
Conceptual clustering is a type of clustering that groups data points based on their similarity to a set of concepts. The algorithm works by first identifying a set of concepts that are relevant to the data. Then, each data point is assigned to the concept that it is most similar to. The concepts are then clustered based on their similarity to each other.
Expectation maximization is an iterative algorithm that is used to estimate the parameters of a statistical model. The algorithm works by alternating between two steps: expectation and maximization. In the expectation step, the algorithm calculates the expected value of the data given the current model parameters. In the maximization step, the algorithm maximizes the likelihood of the data given the current model parameters. This process is repeated until the model parameters converge.
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