共查询到20条相似文献,搜索用时 62 毫秒
1.
给出了空间聚类知识发现的一些基本概念,包括空间聚类维、空间聚类主题及主题相关性度量(相关度和包容度)等。按不同的空间聚类主题进行聚类分析,并以聚类结果的空间样本为纽带,以计算的主题相关性度量为评价标准,对相异空间聚类主题问可能的关联关系进行知识挖掘发现,获得了良好的结果。 相似文献
2.
3.
DBSCAN空间聚类算法及其在城市规划中的应用 总被引:4,自引:1,他引:3
空间聚类是空间数据挖掘和知识发现的主要方法之一。DBSCAN算法可以从带有“噪声”的空间数据库中发现任意形状的聚类,是一种较好的聚类算法。本文介绍了DBSCAN算法的基本概念和原理,并应用GIS二次开发组件MapObjects予以了实现。然后,本文将该算法应用于城市规划中,对某城市中小学和商业网点等公共设施的分布进行了聚类分析,并根据聚类结果对城市规划设计规范中的某些条款进行了讨论。 相似文献
4.
针对传统聚类算法在处理时空位置数据挖掘时面临的多维聚类问题,提出了动态加权聚类模型。该模型叠加利用经典k-均值和基于密度的DBSCAN聚类算法,通过计算最大轮廓系数确定合适的簇数目,按照划分初始簇类、识别和剔除噪声点、修正聚类簇中心点位置坐标3个步骤实现对大体量多维时空位置数据的聚类分析,提出了动态权重系数计算公式,优化了基于密度的DBSCAN聚类算法中相似度函数,并在Python3.7环境下以网络签到数据集实例仿真验算了该模型算法。实验结果表明,相较单一的传统聚类算法,该模型能综合利用多维非位置属性对时空位置数据点聚类,更合理界定聚类簇的归属数据点,对提升时空位置数据集聚类簇中数据点的聚类效果明显。 相似文献
5.
6.
针对Delaunay三角网空间聚类存在的不足,提出一种顾及属性空间分布不均的空间聚类方法。首先将Delaunay三角网空间位置聚类作为约束条件,采用广度优先搜索方法,以局部参数"属性变化率"作为阈值识别非空间属性相似簇的聚类过程。以城市商业中心为例,验证了该方法能够更客观地识别非空间属性相似的簇,且自适应属性阈值可以满足不同聚类需求,为城市商业中心等空间实体的提取提供了一种有效方法。 相似文献
7.
区域划分是依据人口和社会经济指标将行政统计单元或其他地理实体划分成若干个不同水平或类别的集合。由于大多数的人口和社会经济指标来源于面状数据-行政统计单元,常用的区域划分的空间聚类方法是基于面状数据的,本文通过分析现有面状数据的聚类算法特点和不足,进而提出一种新的算法,该方法提出将面状统计单元进行网格划分,引入基于网格密度聚类算法的思想,克服现有面状聚类的诸多缺点,打破行政区划的限制,更好地发现潜在信息。 相似文献
8.
9.
10.
11.
Spatial clustering is widely used in many fields such as WSN (Wireless Sensor Networks), web clustering, remote sensing and so on for discovery groups and to identify interesting distributions in the underlying database. By discussing the relationships between the optimal clustering and the initial seeds, a clustering validity index and the principle of seeking initial seeds were proposed, and on this principle we recommend an initial seed-seeking strategy: SSPG (Single-Shortest-Path Graph). With SSPG strategy used in clustering algorithms, we find that the result of clustering is optimized with more probability. At the end of the paper, according to the combinational theory of optimization, a method is proposed to obtain optimal reference k value of cluster number, and is proven to be efficient. 相似文献
12.
Spatial clustering is widely used in many fields such as WSN (Wireless Sensor Networks), web clustering, remote sensing and so on for discovery groups and to identify interesting distributions in the underlying database. By discussing the relationships between the optimal clustering and the initial seeds, a clustering validity index and the principle of seeking initial seeds were proposed, and on this principle we recommend an initial seed-seeking strategy: SSPG (Single-Shortest-Path Graph). With SSPG strategy used in clustering algorithms, we find that the result of clustering is optimized with more probability. At the end of the paper, according to the combinational theory of optimization, a method is proposed to obtain optimal reference k value of cluster number, and is proven to be efficient. 相似文献
13.
空间聚类是将空间实体根据某些相似的特性聚类成为一个集合,这个集合称为簇。本文研究了一种基于中心点距离的居民地面要素聚类算法:通过获取面状要素的数据,运用基于其几何中心的距离计算方法,判断面要素之间距离的可达性,并将距离小于阈值的面要素进行聚类,最终以凸包的形式将该集合绘制出来。本文的算法是在VS2010以及ArcGIS Engine开发环境下通过编程实现,并进行多组实验,实验结果表明,该应用程序可以实现居民地面要素的自动聚类。 相似文献
14.
Spatial objects have two types of attributes: geometrical attributes and non-geometrical attributes, which belong to two different attribute domains (geometrical and non-geometrical domains). Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clustering: local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted. Second, local features from each site are sent to a central site where global clustering is obtained based on those features. Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient. 相似文献
15.
ZHOU Jiaogen GUAN Jihong LI Pingxiang 《地球空间信息科学学报》2007,10(2):137-144
Spatial objects have two types of attributes: geometrical attributes and non-geometrical attributes, which belong to two different attribute domains (geometrical and non-geometrical domains). Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clus- tering: local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted. Second, local features from each site are sent to a central site where global clustering is obtained based on those features. Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient. 相似文献
16.
遥感影像模糊聚类方法可以在无需样本分布信息的情况下获取比硬聚类方法更高的分类精度,但其仍依赖先验知识来确定影像地物的类别数。本文提出了一种基于自适应差分进化的遥感影像自动模糊聚类方法,该方法利用差分进化搜索速度快、计算简单、稳定性高的优点,以Xie-Beni指数为优化的适应度函数,在无需先验类别信息的情况下自动判定图像的类别数,并结合局部搜索算子对遥感影像进行最优化聚类。通过模拟影像以及两幅真实遥感图像的分类实验表明,本文方法不仅可以正确地自动获取地物类别数,而且能够获得比K均值、ISODATA以及模糊K均值方法更高的分类精度。 相似文献
17.
围绕国家基础地理信息 1∶5 万核心地形要素数据库内容的完善,对不同专业领域和部门,通过问卷进行调研,了解地理信息用户对于国家基础地理信息的要素内容、属性信息等的需求。针对反馈结果,进行了统计分析,并提出了3种不同的数据库内容完善方案。方案的最终确定还需要采取空间分析的手段进行修正,使之可以作为数据库内容完善的参考。 相似文献
18.
A partitional clustering-based segmentation is used to carry out supervised classification for hyperspectral images. The main contribution of this study lies in the use of projected and correlation partitional clustering techniques to perform image segmentation. These types of clustering techniques have the capability to concurrently perform clustering and feature/band reduction, and are also able to identify different sets of relevant features for different clusters. Using these clustering techniques segmentation map is obtained, which is combined with the pixel-level support vector machines (SVM) classification result, using majority voting. Experiments are conducted over two hyperspectral images. Combination of pixel-level classification result with the segmentation maps leads to significant improvement of accuracies in both the images. Additionally, it is also observed that, classified maps obtained using SVM combined with projected and correlation clustering techniques results in higher accuracies as compared to classified maps obtained from SVM combined with other partitional clustering techniques. 相似文献
19.
高国勇 《测绘与空间地理信息》2015,(6):98-100
聚类是数据挖掘的重要分支之一,引入模糊理论的模糊聚类分析为显示数据提供了模糊处理能力,在许多领域被广泛应用。本文应用考虑邻域关系的约束模糊C均值(Fuzzy C-Means with Constrains,FCM_S)算法,将邻域像素引入到目标函数中,进而有效地利用邻域像素信息,提高分割精度。本文应用FCM_S算法对模拟彩色纹理图像进行分割,计算其混淆矩阵,定性定量地与FCM算法进行对比分析,证明了该算法的鲁棒性。 相似文献
20.
Extracting meaningful information from the growing quantity of spatial data is a challenge. The issues are particularly evident with spatio-temporal data describing movement. Such data typically corresponds to movement of humans, animals and machines in the physical environment. This article considers a special form of movement data generated through human–computer interactions with online web maps. As a user interacts with a web map using a mouse as a pointing tool, invisible trajectories are generated. By examining the spatial features on the map where the mouse cursor visits, a user's interests and experience can be detected. To analyse this valuable information, we have developed a geovisual analysis tool which provides a rich insight into such user behaviour. The focus of this paper is on a clustering technique which we apply to mouse trajectories to group trajectories with similar behavioural properties. Our experiments reveal that it is possible to identify experienced and novice users of web mapping environments using an incremental clustering approach. The results can be used to provide personalised map interfaces to users and provide appropriate interventions for completing spatial tasks. 相似文献