Incremental Delaunay triangulation construction for clustering

In this paper, we propose an original solution to the problem of point cloud clustering. The proposed technique is based on a d-dimensional formulated Delaunay Triangulation (DT) construction algorithm and adapts it to the problem of cluster detection. The introduced algorithm allows this detection as along with the DT construction. Precisely, a criterion that detects occurrences of gaps in the simplex perimeter distribution is added during the incremental DT construction. This detection allows to label simplices as being inter- or intra cluster. Experimental results on 2D shape datasets are presented and discussed in terms of cluster detection and topological relationship preservation.