Data analysis depends not only on appropriate methods, but also on interpretation of results. We present an approximate CUR matrix decomposition algorithm which provides intuitive interpretation in terms of the original data for applications and could potentially be used as an alternative to principal component analysis. Our algorithm utilizes convex optimization problems to determine the matrices C and R, and we discuss a strategy for solving these problems when the original data matrix is large. Applications will also be presented.