Many methods are used to spatial data mining.
空间数据挖掘有许多种方法。
One spatial data mining method LMSCMO for linear belts extracting is proposed.
提出了一种线性条带挖掘方法LMSCMO。
Comparison between spatial data mining and traditional data mining is presented.
对空间数据挖掘与传统数据挖掘进行比较。
The paper researches two aspects that are methods and problems of spatial data mining.
本文主要就空间数据挖掘方法和空间数据挖掘存在的问题这两方面进行了研究。
Abstract: spatial outlier detection is a research hotspot in the domain of spatial data mining.
摘要：空间离群模式探测是空间数据挖掘的一个研究热点。
Spatial data mining technology offers valuable means for discovering knowledge in environmental database.
空间数据挖掘技术为环境数据库的知识发现提供了有效的途径。
Visualization mode of spatial data and integration of spatial data mining and visualization are discussed.
并讨论了空间数据可视化表达模式和可视化与空间数据挖掘的整合。
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。
Development and applications of spatial data mining systems have significant advancements over the recent years.
近年来，空间数据挖掘系统的开发及应用已取得了极大的进展。
This paper places great emphasis on the study of the spatial data mining methods and their integration with GIS.
本文重点对空间数据挖掘算法及其与GIS的集成进行了研究。
In the paper, based on hyper graph theory, a hypergraph model is proposed, which is useful for spatial data mining.
该文基于超图理论提出了超图模型并将其用于空间数据挖掘。
Spatial Data Mining (SDM) is a technology which can extract interested and hidden knowledge and spatial relationships.
空间数据挖掘(SDM)是从空间数据中抽取令人感兴趣的、隐含的知识和空间关系。
Active Spatial data mining technology is used in the processes of alarm data fusion, data mining and knowledge discovery.
其中主动空间数据挖掘技术主要体现在数据融合，数据挖掘和知识发现的过程中。
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 transforms between qualitative concepts and their quantitative expressions play an important role in spatial data mining.
定性概念和定量数据之间的转换在空间数据挖掘中起了至关重要的作用。
Aiming to property feature of database in spatial data mining, mining algorithm based on binary is used to spatial data mining.
针对空间数据挖掘中数据库的属性特点，将基于二进制的挖掘算法应用于空间数据挖掘中。
Spatial data mining is a promising field, where the research work on spatial data classification is still in its initial stage.
空间数据采掘是一个很有发展前景的领域，其中空间数据分类的研究尚处在起步阶段。
Spatial data mining in certain spatial data warehouse has become so animated in the field of data warehouse and information decision.
在空间数据仓库中进行空间数据挖掘，成为了当前数据仓库和信息决策领域的前沿研究和热点。
The main task of spatial data mining is to discovery the implicit, previous unknown, and potential useful information from these data.
如何发现在大型空间数据库中所隐藏的、预先未知的信息以辅助相应的应用，这就是目前空间数据挖掘的任务。
This thesis presents its application in spatial data mining and knowledge discovery, and focuses on the cloud models and their algorithms.
针对云理论在空间数据挖掘和知识发现中的应用，提出了基于半云和梯形云的空间距离概念的划分方法。
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。
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。
In order to overcome the bottleneck, spatial data mining was proposed under the umbrella of data mining. Now, a growing attention has been paid to it.
为了突破利用空间数据的瓶颈，人们开始关注数据挖掘中的空间数据挖掘。
In the current tourism management information system, data attribute is only, and spatial data mining, tourist market forecasting and tourist resource evaluating are insufficient;
目前的研究存在以下问题：旅游管理信息系统中旅游信息数据属性单一，在空间数据的挖潜利用以及旅游市场预测、旅游资源评价存在不足；
Spatial Data Mining, one branch of Data Mining, has been widely applied in the geographical information system, remote sensing, navigation, environmental research as well as other field.
空间数据挖掘是数据挖掘的一个分支，在地理信息系统、遥感、导航、环境研究以及许多使用空间数据的领域中有着广泛的应用。
The way of generating frequent candidate a nd pruning technology are difficult technical problem when prenest traditional association rules mining algorithm is used to spatial data mining.
现有的传统关联规则挖掘算法构建频繁候选项的方式和修剪技术是其应用于空间数据挖掘的技术难题。
After studying the analysis and comparison of the realization techniques of spatial database system and spatial data mining systems, we propose a development model of spatial data mining system.
在对空间数据库系统实现技术及空间数据挖掘系统等进行比较分析的基础上，提出了一种空间数据挖掘系统的实现模式。
As a hot topic in present GIS research, and as one of the ways of spatial data mining, the spatial autocorrelation focuses on spatial data convergence and shows that everything is related with others.
空间自相关是地理信息科学目前研究的热点问题，作为空间数据挖掘的一种手段，它着重分析了空间实体的聚集程度，阐释了事物之间普遍联系的准则。
In this way, we can make use of good methods and ideas in classical data mining to discover spatial knowledge under well-pleasing result.
这样可以充分借鉴经典数据挖掘的方法和思想来实施对空间数据进行挖掘，获得较高的效率和满意的结果。
In this way, we can make use of good methods and ideas in classical data mining to discover spatial knowledge under well-pleasing result.
这样可以充分借鉴经典数据挖掘的方法和思想来实施对空间数据进行挖掘，获得较高的效率和满意的结果。
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