Clustering Spatial Data in The Presence of Obstacles

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.

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