CLUSTERING SPATIAL DATA IN THE PRESENCE OF OBSTACLES
Xin Wang, Howard J. Hamilton · International Journal of Artificial Intelligence Tools · 2005
Dealing with constraints due to obstacles is an important topic in constraint-based spatial clustering. In this paper, we proposed the DBRS_O method to identify clusters in the presence of intersected obstacles. Without doing any preprocessing, DBRS_O processes the constraints during clustering. DBRS_O can also avoid unnecessary computations when obstacles do not affect the clustering result. As well, DBRS_O can find clusters with arbitrary shapes, varying densities, deal with significant non-spatial attributes and handle large datasets.