  Recently, sparse subspace clustering has been a valid tool to deal with
high-dimensional data. There are two essential steps in the framework of sparse
subspace clustering. One is solving the coefficient matrix of data, and the
other is constructing the affinity matrix from the coefficient matrix, which is
applied to the spectral clustering. This paper investigates the factors which
affect clustering performance from both clustering accuracy and stability of
the approaches based on existing algorithms. We select four methods to solve
the coefficient matrix and use four different ways to construct a similarity
matrix for each coefficient matrix. Then we compare the clustering performance
of different combinations on three datasets. The experimental results indicate
that both the coefficient matrix and affinity matrix have a huge influence on
clustering performance and how to develop a stable and valid algorithm still
needs to be studied.
