Using a radiosity method to estimate light inter-reflections within large scenes still remains a difficult task. The two main reasons are: (i) the computations entailed by the radiosity method are time consuming and (ii) the large amount of memory needed is very large. In this paper, we address this problem by proposing a new clustering technique as well as a new method of visibility computation for complex indoor scenes. Our clustering algorithm groups polygons that are close to each other in each room (or corridor) of the building. It relies on a classification method of k-mean type and allows the use of several kinds of distance functions. For each group of polygons (or cluster), we estimate the set of potentially visible clusters with the help of openings such as doors or windows. This computation results in a graph in which the nodes correspond to clusters and the edges express visibility relationships between the corresponding clusters. We use this graph for computing radiosity ...