An Improved Density-based Spatial Clustering Algorithm with Sampling
一种改进的基于密度的抽样聚类算法

DBSCAN is one of the effective spatial clustering algorithms,which can discover clusters of any arbitrary shape and handle the noise effectively.However,it has also several disadvantages.First,it is based on only spatial attributes without considering non-spatial attributes in the databases.Second,when DBSCAN handles large-scale spatial databases,it requires large volume of memory support and I/O cost.In this paper,an improved density-based spatial clustering algorithm with sampling(IDBSCAS) is developed,which not only clusters large-scale spatial databases effectively,but also considers spatial attributes and non-spatial attributes.Experimental results of 2-D spatial datasets show that the new algorithm is feasible and efficient.