  Spectral Method is a commonly used scheme to cluster data points lying close
to Union of Subspaces by first constructing a Random Geometry Graph, called
Subspace Clustering. This paper establishes a theory to analyze this method.
Based on this theory, we demonstrate the efficiency of Subspace Clustering in
fairly broad conditions. The insights and analysis techniques developed in this
paper might also have implications for other random graph problems. Numerical
experiments demonstrate the effectiveness of our theoretical study.
