GIS系统中的空间数据挖掘查询语言Spatial Data Mining Querie language in a GIS System |
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课程网址: | http://videolectures.net/solomon_appice_sdm/ |
主讲教师: | Annalisa Appice |
开课单位: | 巴里大学 |
开课时间: | 2007-03-15 |
课程语种: | 英语 |
中文简介: | GIS的优势在于提供了一个丰富的数据基础结构,可通过使用空间排列(例如,邻近性)以有意义的方式组合不同的数据。作为工具箱,GIS使计划人员可以使用地理处理功能(例如地图叠加,连通性测量或主题地图着色)执行空间分析。虽然,这样可以有效地对单个变量进行地理可视化,但很容易忽略复杂的多变量依存关系。使GIS超越自动制图工具的必要步骤是将分析和汇总大量地理参考变量的功能整合到单个预测或评分中。这就是数据挖掘有望带来巨大潜在利益的地方,也是GIS与数据挖掘之间如此紧密配合的原因。 INGENS(感应式地理信息系统)是一个原型GIS,它集成了数据挖掘工具以帮助用户完成地形图解释任务。空间数据挖掘过程的目标是通过挖掘查询语言编写的查询来控制过程参数的用户。在本次演讲中,我将介绍SDMQL(空间数据挖掘查询语言),这是INGENS中使用的空间数据挖掘查询语言。当前,SDMQL支持两项数据挖掘任务:归纳分类规则和发现关联规则。对于这两个任务,该语言都允许指定任务相关数据,要开采的知识种类,背景知识和层次结构,兴趣度度量以及所发现模式的可视化。对查询语言的一些约束由特定的挖掘任务标识。我描述了查询语言的语法,最后简要说明了在实际地图存储库中的应用。 |
课程简介: | The strength of GIS is in providing a rich data infrastructure for combining disparate data in meaningful ways by using a spatial arrangement (e.g., proximity). As a toolbox, a GIS allows planners to perform spatial analysis using geo-processing functions such as map overlay, connectivity measurements or thematic map coloring. Although, this makes effective the geographic visualization of individual variables, complex multi-variate dependencies are easily overlooked. The required step to take GIS beyond a tool for automating cartography is to incorporate the ability of analyzing and condensing a large number of geo-referenced variables into a single forecast or score. This is where data mining promises great potential benefits and the reason why there is such a hand-in-glove fit between GIS and data mining. INGENS (INductive GEographic iNformation System) is a prototype GIS which integrates data mining tools to assist users in their task of topographic map interpretation. The spatial data mining process is aimed at a user who controls the parameters of the process by means of a query written in a mining query language. In this talk, I present SDMQL (Spatial Data Mining Query Language), a spatial data mining query language used in INGENS. Currently, SDMQL supports two data mining tasks: inducing classification rules and discovering association rules. For both tasks the language permits the specification of the task-relevant data, the kind of knowledge to be mined, the background knowledge and the hierarchies, the interestingness measures and the visualization for discovered patterns. Some constraints on the query language are identified by the particular mining task. I describe the syntax of the query language and finally I briefly illustrate the application to a real repository of maps. |
关 键 词: | 数据基础; 连通性测量; 自动制图 |
课程来源: | 视频讲座网 |
最后编审: | 2019-09-22:cwx |
阅读次数: | 88 |