This paper presents a simple new algorithm that performs k-means clustering in one scan of a dataset, while using a buffer for points from the dataset of fixed size. Experiments show that the new method is several times faster than standard k-means, and that it produces clusterings of equal or almost equal quality. The new method is a simplification of an algorithm due to Bradley, Fayyad and Reina that uses several data compression techniques in an attempt to improve speed and clustering quality. Unfortunately, the overhead of these techniques makes the original algorithm several times slower than standard k-means on materialized datasets, even though standard k-means scans a dataset multiple times. Also, lesion studies show that the compression techniques do not improve clustering quality. All results hold for 400 megabyte synthetic datasets and for a dataset created from the real-world data used in the 1998 KDD data mining contest. All algorithm implementations and experiments are designed so that results generalize to datasets of many gigabytes and larger.