Effective clustering and boundary detection algorithm based on Delaunay triangulation

In this paper, a new spatial clustering algorithm TRICLUST based on Delaunay triangulation is proposed. This algorithm treats clustering task by analyzing statistical features of data. For each data point, its values of statistical features are extracted from its neighborhood which effectively models the data proximity. By applying specifically built criteria function, TRICLUST is able to effectively handle data set with clusters of complex shapes and non-uniform densities, and with large amount of noises. One additional advantage of TRICLUST is the boundary detection function which is valuable for many real world applications such as geo-spatial data processing, point-based computer graphics, etc.