  As a kind of basic machine learning method, clustering algorithms group data
points into different categories based on their similarity or distribution. We
present a clustering algorithm by finding hyper-planes to distinguish the data
points. It relies on the marginal space between the points. Then we combine
these hyper-planes to determine centers and numbers of clusters. Because the
algorithm is based on linear structures, it can approximate the distribution of
datasets accurately and flexibly. To evaluate its performance, we compared it
with some famous clustering algorithms by carrying experiments on different
kinds of benchmark datasets. It outperforms other methods clearly.
