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 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.
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.
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。
This paper places great emphasis on the study of the spatial data mining methods and their integration with GIS.
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。
Development and applications of spatial data mining systems have significant advancements over the recent years.
空间关联规则是空间数据中重要的隐含信息，本文采用数据挖掘的方法研究空间关联规则信息的提取。
Spatial Association Rules is important information of implying in the data, this paper adopts method research Spatial Association Rules abstraction of message that data excavate.
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 analysis is important method and study content of spatial analysis and 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。
The transforms between qualitative concepts and their quantitative expressions play an important role in spatial data mining.
空间关联规则挖掘可应用于发现空间数据库中大量空间谓词与非空间谓词之间的特定空间关系。
Mining spatial association rules can be used to discover the specific spatial relationship between spatial predicate and non-spatial predicate in the spatial database.
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。
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.
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。
Active Spatial data mining technology is used in the processes of alarm data fusion, data mining and knowledge discovery.
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。
Aiming to property feature of database in spatial data mining, mining algorithm based on binary is used to spatial data mining.
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。
This thesis presents its application in spatial data mining and knowledge discovery, and focuses on the cloud models and their algorithms.
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。
Spatial data mining technology offers valuable means for discovering knowledge in environmental database.
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。
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.
空间关联规则挖掘是在空间数据库中进行知识发现的一类重要问题。
Spatial association rule discovery in spatial databases is a very important data mining task.
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。
The paper researches two aspects that are methods and problems of spatial data mining.
姚君杰和他的指导老师在做的项目是将现有的数据挖掘算法与IBM的空间数据库软件开发包相整合。
Jay and his mentor are trying to integrate the existing data mining algorithm to the IBM spatial database software development kit.
将空间数据库转换成空间事务数据库是空间关联规则挖掘过程的关键步骤。
This paper is focused on the methods of the construction of spatial transaction database, which is a crucial ste Pin the spatial association rules mining.
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。
Spatial clustering is one of the important research topic in spatial data mining, it is widely applied in spatial analysis.
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。
Visualization mode of spatial data and integration of spatial data mining and visualization are discussed.
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 data mining is a research branch of data mining, and the spatial clustering analysis is an important area of research of spatial data mining.
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。
Cluster analysis is a method of spatial data mining. Clustering algorithm can find some useful clustering structures directly from spatial data base.
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。
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.
摘要：空间离群模式探测是空间数据挖掘的一个研究热点。
Abstract: spatial outlier detection is a research hotspot in the domain of spatial data mining.
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。
Spatial data mining in certain spatial data warehouse has become so animated in the field of data warehouse and information decision.
空间数据可以通过意念制图、图形分析、图谱分析、空间统计等处理发现挖掘得到新的信息。
Map analysis may reveal new information in spatial data, and the map could be mental map, geometric map, summary map and so on.
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。
Spatial Data Mining (SDM) is a technology which can extract interested and hidden knowledge and spatial relationships.
空间数据挖掘中所依赖的空间相关性是由空间关联规则描述的。
The spatial dependence in data mining is normally represented by spatial association rules, which provide the critical information in assessing spatial correlations in large spatial databases.
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。
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。
The main task of spatial data mining is to discovery the implicit, previous unknown, and potential useful information from these data.
应用推荐