Due to the curse of dimensionality, traditional clustering methods usually fail to produce meaningful results for the high dimensional data. Hypergraph partition is believed to be a promising method for dealing with this challenge. In this paper, we first construct a graph <i>G</i> from the data by defining an adjacency relationship between the data points using Shared Reverse k Nearest Neighbors (SRNN). Then a hypergraph is created from the graph <i>G</i> by defining the hyperedges to be all the maximal cliques in the graph <i>G</i>. After the hypergraph is produced, a powerful hypergraph partitioning method called dense subgraph partition (DSP) combined with the k-medoids method is used to produce the final clustering results. The proposed method is evaluated on several real high-dimensional datasets, and the experimental results show that the proposed method can improve the clustering results of the high dimensional data compared with applying k-medoids method directly on the original data.