Most spatial clustering algorithms deal with the objects in Euclidean space. In many real applications, however, the accessibility of spatial objects is constrained by spatial networks (e.g. road network). It is therefore more realistic to work on clustering objects in a road network. The distance metric in such setting is redefined by the network distance, which has to be computed by the expensive shortest path distance over the network. The existing methods are not applicable to such cases. Therefore, by exploiting unique features of road networks, two new clustering algorithms are presented, which use the information of nodes and edges in the network to prune the search space and avoid some unnecessary distance computations. The experimental results indicate that the algorithms achieve high efficiency for clustering objects in real road network.