Research of K value optimization of spatial clustering.
典型空间聚类问题的k值优化研究。
DBSCAN is a spatial clustering algorithm based on density.
DBSCAN是一个基于密度的聚类算法。
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。
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。
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。
An adaptive color signal spatial clustering technique based on area correlation is used and thus the target detection effect is improved.
使用基于区域关联性的彩色信号自适应空间分集检测技术，提高目标检测效果。
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。
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。
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。
Then, the processes of computing the vector values of POI objects are discussed by the methods of questionnaire survey, multi-density spatial clustering and data normalization respectively.
然后，分别讨论了利用问卷调查、多密度空间聚类和数据规格化的方法计算POI对象的各项显著性指标值的过程；
Clustering based to spatial properties has proven to be highly effective at several customer sites.
基于空间属性的聚集在一些客户站点上已经被证明是非常有效的。
The effects of clustering the rows by their spatial properties rely on accessing just a subset of the data during query time.
按照空间属性聚集这些行的效果取决于在查询时对一个数据子集的访问。
The clustering of extended spatial objects is one of the focuses of spatial cluster analysis.
扩展空间对象的聚类分析是空间聚类研究的焦点问题。
Seven kinds of spatial data clustering approaches are studied. And the technique to solve the problem of Constraint-based Spatial Cluster Analysis is explored.
系统研究了七种典型的空间数据聚类方法，积极探索基于约束条件的空间聚类问题的解决方案；
This study, by means of multidimensional scaling and hierarchical clustering, attempts a similar classification of 17 spatial words by the deaf students tested.
以聋人大学生为被试，对17对空间词做相似性分类，用多维标度法和分层聚类法分析。
A novel cluster cell graph was constructed using the nuclei as vertices to characterize and measure spatial distribution and cell clustering.
以细胞核为顶点来描绘测量细胞的空间分布与聚集情况，我们创建了一幅新奇的细胞集落图像。
This paper proposes a solving method of grid granularity in spatial data clustering.
提出一种空间数据聚类中的网格粒度求解方法。
By introducing a new grid-based data compression framework, conducted the study on the clustering algorithm SGRIDS which dealed with a large spatial databases.
引入了一种新的基于网格的数据压缩方法，并应用该方法对处理大型空间数据集的聚类算法SGR IDS进行研究。
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。
The comparative experimental results show that TART2 network is suitable for clustering about the ribbon distribution of spatial data, and it has higher plasticity and adaptability.
对比实验结果表明，TART2网络更适用于带状分布的空间数据聚类，具有较高的可塑性和自适应性。
Results: The spatial distributions of clustering land fragmentation Index results are very significant on the township level of Fenyi County.
研究结果：分宜县各乡镇土地利用细碎度指标的聚类结果空间分布规律特征十分显著。
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。
This paper presents algorithmic principles for approaching clustering of geo-spatial data.
本文介绍了地学空间数据迭代聚类的算法原理。
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。
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。
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