  We give a simple and effective two stage algorithm for approximating a point
cloud $\mathcal{S}\subset\mathbb{R}^m$ by a simplicial complex $K$. The first
stage is an iterative fitting procedure that generalizes k-means clustering,
while the second stage involves deleting redundant simplices. A form of
dimension reduction of $\mathcal{S}$ is obtained as a consequence.
