Spatial Clustering in the Presence of Obstacles
Proceedings of the 17th International Conference on Data Engineering
Clustering Spatial Data in the Presence of Obstacles: a Density-Based Approach
IDEAS '02 Proceedings of the 2002 International Symposium on Database Engineering & Applications
Clustering Spatial Data when Facing Physical Constraints
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Continuous obstructed nearest neighbor queries in spatial databases
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Continuous nearest-neighbor search in the presence of obstacles
ACM Transactions on Database Systems (TODS)
Continuous visible nearest neighbor query processing in spatial databases
The VLDB Journal — The International Journal on Very Large Data Bases
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The previous spatial clustering methods calculate the distance value between two spatial objects using the Euclidean distance function, which cannot reflect the grid path, and their computational complexity is high in the presence of obstacles. Therefore, in this paper, we propose a novel spatial clustering algorithm called DBSCAN-MDO. It reflects the grid path in the real world using the Manhattan distance function and reduces the number of obstacles to be considered by grouping obstacles in accordance with MBR of each cluster and filtering obstacles that do not affect the similarity between spatial objects.