A density-based spatial clustering algorithm considering both spatial proximity and attribute similarity

Geometrical properties and attributes are two important characteristics of a spatial object. In previous spatial clustering studies, these two characteristics were often neglected. This paper addresses the problem of how to accommodate geometrical properties and attributes in spatial clustering. A new density-based spatial clustering algorithm (DBSC) is developed by considering both spatial proximity and attribute similarity. Delaunay triangulation with edge length constraints is first employed for modeling the spatial proximity relationships among spatial objects. A modified density-based clustering strategy is then designed and used to identify spatial clusters. Objects in the same cluster detected by the DBSC algorithm are proximal in a spatial domain and similar in an attribute domain. In addition, the algorithm is able to detect clusters of arbitrary shapes and non-homogeneous densities in the presence of noise. The effectiveness and practicability of the DBSC algorithm are validated using both simulated and real spatial datasets.

► The DBSC algorithm can consider both spatial proximity and attribute similarity.
► The DBSC algorithm can discover clusters of arbitrary shapes, uneven densities.
► Only one input parameter must be user-specified.
► The DBSC algorithm is easily implemented and robust to noise.