The document proposes a fast clustering-based feature selection algorithm (FAST) to efficiently and effectively select useful feature subsets from high-dimensional data. FAST works in two steps: (1) it clusters features using minimum spanning trees, partitioning clusters so each represents a subset of independent features; (2) it selects the most representative feature from each cluster to form the output subset. Experiments on 35 real-world datasets show FAST not only selects smaller feature subsets but also improves performance of four common classifiers compared to other feature selection methods.