In this paper, we propose an original solution tothe problem of point cloud clustering. The proposed techniqueis based on a d-dimensional formulated Delaunay Triangulation(DT) construction algorithm and adapts it to the problem ofcluster detection. The introduced algorithm allows this detectionas along with the DT construction. Precisely, a criterion thatdetects the occurrences of gaps in the simplices perimeterdistribution within the triangulation is added during incrementalDT construction. This allows then to detect and label simplices asinter- or intra cluster. Experimental results on 2D shape datasetsare presented and discussed in terms of cluster detection andtopological relationship preservation.