A company wants to segment customers based on demographics and purchasing patterns. Which algorithm is most appropriate for discovering such groups?

K-means is a clustering algorithm, making it ideal for discovering inherent groupings or segments within unlabeled data, such as customer demographics and purchasing patterns. It partitions data points into K clusters, where each data point belongs to the cluster with the nearest mean. K-nearest neighbors (k-NN) is a supervised classification algorithm used for predicting the class of a new data point based on its neighbors, not for discovering groups. A Decision tree is also a supervised algorithm used for classification or regression by creating a tree-like model of decisions. Support vector machine (SVM) is a supervised algorithm primarily used for classification and regression tasks by finding an optimal hyperplane that separates data points into classes.