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。
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；
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。
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。
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。
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。
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；
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。
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。
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。
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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