DBSCAN is a spatial clustering algorithm based on density.
DBSCAN是一个基于密度的聚类算法。
Research of K value optimization of spatial clustering.
典型空间聚类问题的k值优化研究。
So we can be considered the use of genetic algorithms to solve the problem of spatial clustering.
因此，可以考虑运用遗传算法来解决空间聚类问题。
Based on mathematical morphology, a new algorithm of 3d spatial clustering was presented, which clustered spatial objects by closure operation.
提出了一个基于数学形态学的三维空间聚类算法。
Spatial data mining is a research branch of data mining, and the spatial clustering analysis is an important area of research of spatial data mining.
空间数据挖掘是数据挖掘的一个研究分支，而空间聚类分析是空间数据挖掘的一个重要的研究领域。
Spatial clustering analysis is important method and study content of spatial analysis and spatial data mining.
空间聚类是空间分析和空间数据挖掘的重要方法和研究内容。
Spatial clustering is one of the important research topic in spatial data mining, it is widely applied in spatial analysis.
空间聚类是空间数据挖掘研究的重点内容之一，被广泛应用在空间数据分析中。
Experimental results show that the new algorithm has better performance than Density Based Spatial Clustering of Applications with Noise (DBSCAN).
实验结果表明，新算法较基于密度的带噪声数据应用的空间聚类方法(DBSCAN)具有更好的聚类性能。
Experimental results show that the new algorithm has better performance than Density Based Spatial Clustering of Applications with Noise (DBSCAN).
实验结果表明，新算法较基于密度的带噪声数据应用的空间聚类方法(DBSCAN)具有更好的聚类性能。
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