【作者】 刘潭仁；

【导师】 郭平；

【作者基本信息】 重庆大学 ， 计算机应用技术， 2004， 硕士

【摘要】 空间数据挖掘是数据挖掘(Data Mining)的一个分支领域,它在遥感(Remote Sense)和地理信息系统(Geographic Information System, GIS)中有着广泛的应用。从GIS中进行空间数据挖掘所发现的知识、可用于对空间数据(Spatial Data)的理解、空间知识库(Spatial Repository)的构造、空间数据库(Spatial Database)的重组和空间查询(Spatial Query)的优化等。在GIS系统中融合空间数据挖掘等技术构建的空间决策支持系统,在资源调查、评价、管理和监测,城市的管理、规划和市政工程、行政管理与空间决策,灾害的评估与预测、地籍管理及土地利用、交通、农业、公安等诸多领域发挥重要作用。本论文结合项目主要对空间数据挖掘技术和空间决策支持系统进行了如下几个方面的研究:首先对决策支持系统(Decision Support System,DSS)发展和决策支持系统体系结构进行了研究,提出了综合分布式空间决策支持系统体系结构,着重研究了分布式数据挖掘系统的系统结构。其次介绍了粗糙集(Rough Set)和遗传算法(Genetic Algorithm)的基本理论与方法,阐述了空间数据获取和利用粗糙集进行空间数据预处理的方法。第三介绍了基于粗糙集和遗传算法的数据挖掘改进算法——基于属性核的遗传粗糙约简算法和其并行算法。给出了基于图结构的候选序列生成算法和Hib&Dim-FP算法。在论文给出的空间数据挖掘模型下,这些算法可用于空间数据挖掘。第四就空间规则的评价和可视化解析技术进行了研究,结合多维可视化技术和地理信息系统,概括出了空间规则评价和可视化解释方案。最后利用小三峡监管原型系统验证了论文提出的各种理论和方案。原型系统实现了地物提取模型,伪彩色模型和空间预测报警三个业务模型。总之,论文提出了一种利用粗糙集和遗传算法进行空间数据挖掘的方法,并通过原型系统论证了方法的可行性。论文的研究工作为项目的具体实施提供了关键技术指导。更多还原

【Abstract】 Spatial data mining is a branch of data Mining, and is widely used in remote sense and geographic information system. Knowledge discovered by spatial data mining in GIS can be used for comprehension of spatial data, construction of spatial repository, reorganization of spatial database, optimization of spatial query and so on. The SDSS(Spatial Decision Support System) ,which combined with GIS ,spatial data mining and other intelligence tech, play a very important role in investigating, evaluation, and monitoring of resources, city management and planning, administration, spatial decision, evaluation and predication of disaster, cadastre management and land exploitation, traffic, agriculture, police and etc. The research work of the Dissertation is involved in spatial data mining and SDSS and the main contents are as follows:Firstly,we investigate the development and the system framework of DDS (Decision Support System ), suggest a system framework for the synthetic, distributed SDSS(Spatial Data Mining System) , and then especially study the system frameworks of distributed DSS. Secondly, the basic theories and methods of rough set and genetic algorithm are introduced. Otherwise the method of getting spatial data and the spatial data pre-treating approach based on rough set are discussed.Thirdly, a improved DM algorithm,genetic-enhanced core attributes based reduct method, and its distributed algorithm are introduced. In addition, two algorithms including Graph-based candidate frequent patterns generating algorithm and Hib&Dim-FP are proposed. All the algorithms can apply to spatial data mining under the spatial data-mining model given in the article.Then, techniques of visualization about evaluating and interpreting spatial rules are studied. Combined with multi-dimension techniques of visualization and GIS, a method of evaluating and interpreting spatial rules are proposed.Lastly, a prototype system named little three gorges prototype system validates theories and methods discussed in the article. The prototype system implements three business models including substance extracting model, pseudocolor model and spatial predicting model。 <WP=7>Above all, a RS&GA based spatial data mining method is proposed and the feasibility of the method are validated by a prototype system. The research of the article supplies key techniques support for real implementation of the project.更多还原