Obstacle constraint spatial clustering

Yuan-ni Wang; Fu-ling Bian

doi:10.1117/12.837648

14 October 2009 Obstacle constraint spatial clustering

Yuan-ni Wang, Fu-ling Bian

Proceedings Volume 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining; 749217 (2009) https://doi.org/10.1117/12.837648
Event: International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, 2009, Wuhan, China

Abstract

Constraints in the real world must be seriously considered in the process of spatial clustering. In this paper we study the spatial clustering issue in the presence of obstacles. The cluster algorithm is based on the K-medoid algorithm, and an improved algorithm Guo Tao is introduced to obtain the distance of spatial objects in the presence of obstacles. It is more efficient for small and medium-sized data through theoretical analysis. The experiments results prove that the algorithm is feasible.

Citation Download Citation

Yuan-ni Wang and Fu-ling Bian "Obstacle constraint spatial clustering", Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 749217 (14 October 2009); https://doi.org/10.1117/12.837648

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