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一种基于双重距离的空间聚类方法 总被引:10,自引:1,他引:9
传统聚类方法大都是基于空间位置或非空间属性的相似性来进行聚类,分裂了空间要素固有的二重特性,从而导致了许多实际应用中空间聚类结果难以同时满足空间位置毗邻和非空间属性相近。然而,兼顾两者特性的空间聚类方法又存在算法复杂、结果不确定以及不易扩展等问题。为此,本文通过引入直接可达和相连概念,提出了一种基于双重距离的空间聚类方法,并给出了基于双重距离空间聚类的算法,分析了算法的复杂度。通过实验进一步验证了基于双重距离空间聚类算法不仅能发现任意形状的类簇,而且具有很好的抗噪性。 相似文献
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不同尺度、来源的地图上同一要素通常具有一定的相似度。地图空间要素相似度在GIS领域具有广泛的应用。论文在总结前人相关成果的基础上分别从位置(距离)、形状、大小三个方面给出了面状空间要素相似性度量模型:以分形维数和面积/周长(紧凑度)作为相似特征的形状相似度;以中值距离作为相似特征的距离相似度;以面积或周长作为相似特征的大小相似度。最后,以多尺度面状空间要素为实验数据,通过比较分析验证了本文提出的相似性度量模型可行性。实验结果表明:以中值距离、分形维数作为相似度指标的度量模型综合考虑了面状要素局部结构和整体分布,在面状空间要素相似性度量方面具有很好的稳定性。 相似文献
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顾及距离与形状相似性的面状地理实体聚类 总被引:3,自引:0,他引:3
与点状地理实体不同,面状地理实体不仅具有位置特征.还具有形状特征.对于面状地理实体而言,仅考虑距离因素设计聚类准则是不全面的.综合考虑距离和几何形状相似性来设计聚类准则,实现了相应的聚类算法.实验证明,该算法适合面状地理实体的聚类分析. 相似文献
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针对目前只能对单一运动特征(速度、方向等)进行轨迹相似性分析的不足,提出了基于多重运动特征的轨迹相似性度量,该度量对于分析和理解移动对象的运动行为和规律具有重要意义。将其应用于基于多重运动特征的运动序列模式发现。该相似性度量借鉴数据立方体的思想,将多重运动特征时间序列进行量化和符号化表示,在多重运动特征值域空间中计算两字符间的距离作为字符间替换代价,最终以加权编辑距离作为相似性度量。将该相似性度量与谱聚类方法相结合进行运动序列模式发现。实验以飓风数据为例,通过气象文献中飓风的发生与运动规律验证了模型的有效性。 相似文献
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基于格式塔识别原则挖掘空间分布模式 总被引:9,自引:2,他引:9
面向空间群目标的分布模式识别是空间数据挖掘比较关注的问题。本研究基于空间认知原理与视觉识别格式塔完形原则并结合空间聚类方法对该问题进行研究,提出用于描述实体间差异的"视觉距离"概念,其定义综合考虑视觉识别中的位置、方向、大小差异,通过Delaunay三角网计算几何构造建立该距离计算的模型。在实验基础上提出基于最小支撑树MST的聚类方法,获得与视觉认知相一致的结果。研究试图表明一个观念,即通用性的数据处理模型在GIS实际应用时,需要根据GIS作为"空间认知"科学的原理,作技术方法上的改进,需要考虑认知主体在感知、辨析、识别、推理不同思维过程中的认知心理原则。 相似文献
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《国土资源遥感》2017,(2)
遥感影像的变化检测从基于像素到面向对象,从阈值分割到相似性度量已有众多的研究成果;但在对面向对象遥感图像变化检测中,存在分割参数的选择、变化阈值的确定、对象变化程度的表达等问题。为此,提出一种基于相似度测度的面向对象遥感影像变化检测方法,并打破了以往仅以有/无变化的检测结果所呈现的表现形式。首先计算了图像对象分割的最优参数,得到了2个时相的图斑对象,并进行了空间叠加处理;然后利用KL相似度计算方法计算了图斑对象的相似度系数,利用直方图统计了该系数的自然聚类特征;再运用不同的自然聚类特征值,分级得到了图斑对象的变化程度;最后,分析了不同参数分割结果、不同分级方法对图像变化程度检测的影响,同时通过对比有/无变化的检测结果,验证了本研究所提方法的科学性和有效性。 相似文献
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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
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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
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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
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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
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空间数据模糊聚类的有效性(英文) 总被引:1,自引:0,他引:1
The validity measurement of fuzzy clustering is a key problem. If clustering is formed, it needs a kind of machine to verify its validity. To make mining more accountable, comprehensible and with a usable spatial pattern, it is necessary to first detect whether the data set has a clustered structure or not before clustering. This paper discusses a detection method for clustered patterns and a fuzzy clustering algorithm, and studies the validity function of the result produced by fuzzy clustering based on two aspects, which reflect the uncertainty of classification during fuzzy partition and spatial location features of spatial data, and proposes a new validity function of fuzzy clustering for spatial data. The experimental result indicates that the new validity function can accurately measure the validity of the results of fuzzy clustering. Especially, for the result of fuzzy clustering of spatial data, it is robust and its classification result is better when compared to other indices. 相似文献
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Existing methods of spatial data clustering have focused on point data, whose similarity can be easily defined. Due to the complex shapes and alignments of polygons, the similarity between non‐overlapping polygons is important to cluster polygons. This study attempts to present an efficient method to discover clustering patterns of polygons by incorporating spatial cognition principles and multilevel graph partition. Based on spatial cognition on spatial similarity of polygons, four new similarity criteria (i.e. the distance, connectivity, size and shape) are developed to measure the similarity between polygons, and used to visually distinguish those polygons belonging to the same clusters from those to different clusters. The clustering method with multilevel graph‐partition first coarsens the graph of polygons at multiple levels, using the four defined similarities to find clusters with maximum similarity among polygons in the same clusters, then refines the obtained clusters by keeping minimum similarity between different clusters. The presented method is a general algorithm for discovering clustering patterns of polygons and can satisfy various demands by changing the weights of distance, connectivity, size and shape in spatial similarity. The presented method is tested by clustering residential areas and buildings, and the results demonstrate its usefulness and universality. 相似文献
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LIU WenbaoXIA Zongguo DENG Min 《地球空间信息科学学报》2001,4(4):37-42
1 IntroductionTherepresentationofspatialobjectsisoneofthekeyissuesincurrentresearchonthespatialdatabasetheoryofGIS (BurroughandMcDonnell,1 998) .Spatialobjectsinnatureareclassifiedastwokinds:objectswithdistinctiveboundariesandob jectswithtransitionalorfuzzy… 相似文献
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An adaptive dual clustering algorithm based on hierarchical structure: A case study of settlement zoning
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Traditional dual clustering algorithms cannot adaptively perform clustering well without sufficient prior knowledge of the dataset. This article aims at accommodating both spatial and non‐spatial attributes in detecting clusters without the need to set parameters by default or prior knowledge. A novel adaptive dual clustering algorithm (ADC+) is proposed to obtain satisfactory clustering results considering the spatial proximity and attribute similarity with the presence of noise and barriers. In this algorithm, Delaunay triangulation is utilized to adaptively obtain spatial proximity and spatial homogenous patterns based on particle swarm optimization (PSO). Then, a hierarchical clustering method is employed to obtain clusters with similar attributes. The hierarchical clustering method adopts a discriminating coefficient to adaptively control the depth of the hierarchical architecture. The clustering results are further refined using an optimization approach. The advantages and practicability of the ADC+ algorithm are illustrated by experiments on both simulated datasets and real‐world applications. It is found that the proposed ADC+ algorithm can adaptively and accurately detect clusters with arbitrary shapes, similar attributes and densities under the consideration of barriers. 相似文献