附加限定规则的空间聚类方法及应用研究
发布时间：2018-08-01 12:58
【摘要】：空间聚类是空间数据挖掘的重要方法之一，有着十分广泛的应用领域。与规则归类不同，聚类分析无需背景知识，能直接从空间数据库中发现有意义的空间聚类结构，有其无可替代的优越性。本文在总结前人工作的基础上，研究附加限定规则的空间聚类问题，在普通空间聚类方法的基础上为其附加空间限定规则、非空间属性限定规则以及方位限定规则，以使空间聚类更加符合实际应用需求。 本文主要内容包括： 1．分析了空间数据挖掘及空间聚类分析的背景、意义以及相关理论和技术基础。从空间聚类分析的研究背景及意义出发，分析国内外研究进展，并指出当前研究中存在的主要问题。 2．分析了空间聚类分析和空间分级分析的基本概念，给出了空间聚类分析的基本框架及主要算法，并结合聚类分析给出了空间分级分析的基本流程,说明了空间聚类分析与空间分级分析结合使用的实践意义。分析了限定规则问题提出的依据及其必要性，并给出了限定规则问题的定义及相关概念。 3．一般情况下，限定规则可分为两类，空间限定规则以及非空间属性限定规则。在此基础上，，本文提出第三种限定规则，方位限定规则。三种限定规则的度量方式不一样，附加到空间聚类中的方法也不一样，本文在格网空间中进行空间聚类，通过在格网空间中实现三种限定规则的数学运算及转换，将三种限定规则同时附加到空间聚类上，实现三种限定规则共同作用下的空间聚类。 4．分级处理与聚类处理一样都可挖掘出隐藏在数据中的潜在规律，不同的是分级处理需要根据实际需求设定分级指标及其权值。本文研究如何依据限定规则对空间聚类结果进行分级处理。在传统的非空间属性分级基础上，本文提出了方位因素分级，并给出了其实现方法，使得非空间属性规则和方位限定规则同时参与空间分级分析。此外，对这两种分级方式进行统一的数学转换，实现了两种分级因素同时影响下的空间分级方法。 5．设计了基于空间聚类的空间数据挖掘应用实验系统，实现了附加限定规则的空间聚类，聚类结果的分级处理操作。在此基础上，本文提出了空间聚类一个新的应用方向。将空间聚类与空间分级相结合应用于电子地图的兴趣点选择，并结合附加空间限定规则中障碍距离的计算方法，实现了实时交通环境下的路径规划。
[Abstract]:Spatial clustering is one of the important methods of spatial data mining, which has a wide range of applications. Different from rule classification, clustering analysis can directly find meaningful spatial clustering structure from spatial database without background knowledge, which has irreplaceable advantages. In this paper, on the basis of summarizing the previous work, we study the spatial clustering problem of additional restricted rules. On the basis of ordinary spatial clustering methods, we make use of the additional space rules, non-spatial attribute rules and azimuth rules. In order to make space clustering more in line with the actual application needs. The main contents of this paper are as follows: 1. The background, significance, theoretical and technical basis of spatial data mining and spatial clustering analysis are analyzed. Based on the research background and significance of spatial clustering analysis, this paper analyzes the research progress at home and abroad, and points out the main problems existing in the current research. 2. The basic concepts of spatial clustering analysis and spatial hierarchical analysis are analyzed. In this paper, the basic framework and main algorithms of spatial clustering analysis are given, and the basic flow of spatial hierarchical analysis is given, and the practical significance of combining spatial clustering analysis with spatial hierarchical analysis is explained. This paper analyzes the basis and necessity of the problem of qualified rules, and gives the definition and related concepts of the problem of limited rules. 3. In general, the limited rules can be divided into two categories. Space qualification rule and non-spatial attribute qualification rule. On this basis, this paper proposes a third kind of qualification rule, the azimuth rule. The measurement methods of the three kinds of restricted rules are different, and the methods attached to the spatial clustering are also different. In this paper, the spatial clustering is carried out in the grid space, and the mathematical operation and transformation of the three restricted rules are realized in the grid space. Three kinds of restricted rules are attached to spatial clustering at the same time to realize spatial clustering under the joint action of three kinds of restricted rules. 4. Hierarchical processing and clustering processing can mine the potential laws hidden in the data. The difference is that the grading process needs to set the grading index and its weight value according to the actual demand. In this paper, we study how to classify the spatial clustering results according to the limited rules. Based on the traditional classification of non-spatial attributes, this paper puts forward the classification of azimuth factors, and gives its implementation method, which makes the non-spatial attribute rules and azimuth defining rules participate in the spatial classification analysis at the same time. In addition, through the unified mathematical transformation of these two classification methods, the spatial classification method under the influence of two grading factors is realized. 5. A spatial data mining application experiment system based on spatial clustering is designed. Spatial clustering with additional qualification rules and hierarchical processing of clustering results are implemented. On this basis, this paper proposes a new application direction of spatial clustering. The spatial clustering and spatial classification are applied to the selection of points of interest in electronic maps, and the path planning in real-time traffic environment is realized by combining with the calculation method of obstacle distance in additional space restriction rules.
【学位授予单位】：解放军信息工程大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：P208;TP311.13
本文编号：2157653
[Abstract]:Spatial clustering is one of the important methods of spatial data mining, which has a wide range of applications. Different from rule classification, clustering analysis can directly find meaningful spatial clustering structure from spatial database without background knowledge, which has irreplaceable advantages. In this paper, on the basis of summarizing the previous work, we study the spatial clustering problem of additional restricted rules. On the basis of ordinary spatial clustering methods, we make use of the additional space rules, non-spatial attribute rules and azimuth rules. In order to make space clustering more in line with the actual application needs. The main contents of this paper are as follows: 1. The background, significance, theoretical and technical basis of spatial data mining and spatial clustering analysis are analyzed. Based on the research background and significance of spatial clustering analysis, this paper analyzes the research progress at home and abroad, and points out the main problems existing in the current research. 2. The basic concepts of spatial clustering analysis and spatial hierarchical analysis are analyzed. In this paper, the basic framework and main algorithms of spatial clustering analysis are given, and the basic flow of spatial hierarchical analysis is given, and the practical significance of combining spatial clustering analysis with spatial hierarchical analysis is explained. This paper analyzes the basis and necessity of the problem of qualified rules, and gives the definition and related concepts of the problem of limited rules. 3. In general, the limited rules can be divided into two categories. Space qualification rule and non-spatial attribute qualification rule. On this basis, this paper proposes a third kind of qualification rule, the azimuth rule. The measurement methods of the three kinds of restricted rules are different, and the methods attached to the spatial clustering are also different. In this paper, the spatial clustering is carried out in the grid space, and the mathematical operation and transformation of the three restricted rules are realized in the grid space. Three kinds of restricted rules are attached to spatial clustering at the same time to realize spatial clustering under the joint action of three kinds of restricted rules. 4. Hierarchical processing and clustering processing can mine the potential laws hidden in the data. The difference is that the grading process needs to set the grading index and its weight value according to the actual demand. In this paper, we study how to classify the spatial clustering results according to the limited rules. Based on the traditional classification of non-spatial attributes, this paper puts forward the classification of azimuth factors, and gives its implementation method, which makes the non-spatial attribute rules and azimuth defining rules participate in the spatial classification analysis at the same time. In addition, through the unified mathematical transformation of these two classification methods, the spatial classification method under the influence of two grading factors is realized. 5. A spatial data mining application experiment system based on spatial clustering is designed. Spatial clustering with additional qualification rules and hierarchical processing of clustering results are implemented. On this basis, this paper proposes a new application direction of spatial clustering. The spatial clustering and spatial classification are applied to the selection of points of interest in electronic maps, and the path planning in real-time traffic environment is realized by combining with the calculation method of obstacle distance in additional space restriction rules.
【学位授予单位】：解放军信息工程大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：P208;TP311.13
【共引文献】
相关博士学位论文 前1条
1 付强;中国畜养产污综合区划方法研究[D];河南大学;2013年
本文编号：2157653
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