DBSCAN is a spatial clustering algorithm based on density.
DBSCAN是一个基于密度的聚类算法。
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。
Cluster analysis is a method of spatial data mining. Clustering algorithm can find some useful clustering structures directly from spatial data base.
聚类分析是空间数据挖掘的一种方法，聚类算法能从空间数据库中直接发现一些有用的聚类结构。
This algorithm could not only complete 3d spatial clustering at a time, and process clustering in-convex and complicated objects rapidly.
该算法通过闭合运算，将空间对象聚成类，一次完成三维空间聚类，可以快速处理非凸的、复杂的聚类形状。
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。
Proposes an improved DBSCAN algorithm which can handle non-spatial properties and greatly accelerate the speed of clustering.
文中提出了一种基于DBSCAN的算法，可以处理非空间属性，同时又可以加快聚类的速度。
The complexity of time and spatial is becoming the difficulty of K-Means clustering algorithm while it deals with the huge amounts of data sets.
该算法基于图像的特点，利用K均值聚类算法将图像分成几个灰度区间，然后再分别进行均衡化。
Modifying the objective function of FCM and introducing a variable as the parameter to control the tight degree of neighborhood effect present a spatial model to FCM clustering algorithm.
本文改进了传统FCM的目标函数，引入控制邻域作用紧密程度的参数，提出了一种能够更加合理地运用图像的空间信息，改进的模糊c -均值聚类算法。
Modifying the objective function of FCM and introducing a variable as the parameter to control the tight degree of neighborhood effect present a spatial model to FCM clustering algorithm.
本文改进了传统FCM的目标函数，引入控制邻域作用紧密程度的参数，提出了一种能够更加合理地运用图像的空间信息，改进的模糊c -均值聚类算法。
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