To segment customers by demographics and purchasing behavior, which algorithm is best suited for grouping similar customers?

K-means is an unsupervised clustering algorithm ideal for grouping similar data points, like customers, into distinct clusters based on their features (demographics, purchasing behavior). It aims to minimize the variance within each cluster, making it well-suited for customer segmentation. K-nearest neighbors (k-NN) is a supervised classification or regression algorithm used for predicting the class or value of a new data point based on its neighbors, not for grouping unlabeled data. Decision trees are supervised algorithms used for classification or regression by creating a tree-like model of decisions. Support vector machines (SVMs) are supervised algorithms primarily used for classification and regression by finding an optimal hyperplane that separates data points into classes.