Which of the following is an unsupervised data mining algorithm?

K-means is an unsupervised learning algorithm because it groups data into clusters based solely on inherent patterns without using labeled outcomes. Unlike supervised methods that require pre-defined target variables for training, K-means identifies natural structures in unlabeled datasets by minimizing the variance within clusters. Decision Trees, Naïve Bayes, and Support Vector Machines are all supervised algorithms; they rely on labeled training data to learn a mapping from inputs to specific outputs. For instance, Decision Trees classify data by learning rules from known categories, while Naïve Bayes calculates probabilities based on labeled examples. Since K-means operates without prior knowledge of class labels, it is the only unsupervised option listed.