A density-based spatial clustering for physical constraints

作者：Wang Xin^*; Rostoker Camilo; Hamilton Howard J

来源：Journal of Intelligent Information Systems, 2012, 38(1): 269-297.

DOI：10.1007/s10844-011-0154-7

• Spatial data mining
• Constraint-based clustering
• Density-based clustering
• Obstacle
• Facilitator

We propose a spatial clustering method, called DBRS+, which aims to cluster spatial data in the presence of both obstacles and facilitators. It can handle datasets with intersected obstacles and facilitators. Without preprocessing, DBRS+ processes constraints during clustering. It can find clusters with arbitrary shapes. DBRS+ has been empirically evaluated using synthetic and real data sets and its performance has been compared to DBRS and three related methods for handling obstacles, namely AUTOCLUST+, DBCLuC*, and DBRS_O.

• 出版日期2012-2