一种基于空间邻接关系的k-means聚类改进算法

空间对象不仅具有非空间的属性特征，而且具有与空间位置、拓扑结构相关的空间特征。利用传统的聚类方法对空间对象进行聚类时，由于没有考虑空间关系，同一类的对象可能出现在空间不相邻的位置。基于空间邻接关系的k-means改进算法将相邻对象的空间邻接关系作为约束条件加以考虑，使聚类结果既反映了属性特征的相似程度，又反映了对象的空间相邻状态，从而可以揭示不同类别对象的空间分布格局，因此其比传统的k-means方法更适合于空间对象的聚类分析。     

基于邻接图的离群数据聚类算法

离群数据是数据中的小模式,因其固有的少数据与稀疏性等特征,使得基于距离或基于统计等常规聚类方式不适用于对离群数据的分类。该文根据离群对象关键域子空间的重合度,定义了离群共享属性集与离群相似度等概念,提出-离群簇分析技术。通过构建离群邻接图并将其稀疏化,将-离群簇搜索与相应的离群邻接图的最大完全子图搜索一一对应,给出一种基于邻接图的离群数据聚类算法。算例及实验结果表明,该方法具有较高的效率及良好的直观性。     

基于邻接关系的空间数据挖掘

空间邻接关系是空间数据库对象之间的特征联系，其处理过程直接影响着空间数据挖掘算法的实现与效率，基于3种邻接关系，给出了邻接图，邻接路径的概念和几个基本操作，并分析了几种典型的空间数据挖掘算法。     

一种具有方向特点的网络对象聚类算法

对分别采用欧氏距离和网络距离作为相似性测度的聚类方法进行分析,并从空间网络中对象间着手,提出一种具有方向特点的网络对象聚类算法.算法利用空间网络的邻接关系,将两种距离结合起来作为聚类的相似性测度以提高聚类的精度.算法分析和实验证明,该算法的聚类效果优于单一度量的聚类方法.     

满足二维空间邻接条件的遗传聚类算法研究

分析了目前满足二维空间邻接条件聚类算法的不足。从聚类概念出发,重新定义了满足二维空间邻接条件聚类的概念。面对满足二维空间邻接条件的聚类问题,定义了邻接矩阵的概念。以邻近距离和邻接矩阵为样本划分依据,以类内平方误差和（WGSS）为聚类目标函数,引入遗传算法,提出满足二维空间邻接条件的遗传聚类算法。通过实例进行了算法测试,并与模糊聚类（FCM）结果进行比较。     

高效的大型图聚类方法研究

对于大规模的图数据,当前的图聚类算法的时间和空间扩展性较差,且倾向于细粒度的簇.本文提出k层邻接点概念,从而避免单层邻接点导致的聚类细化.提出一种基于割集的分布式聚类算法,通过连通性判断搜索最小代价割集,从而降低图分片的关联性,提高算法的并行度和可扩展性.通过实际数据集上的大量实验表明,本文所提出的聚类方法较传统方法在时间和空间效率上具有较大优势,并且可以发现更高质量的簇.     

高维分类型数据加权子空间聚类算法

子空间聚类是高维数据聚类的一种有效手段,子空间聚类的原理就是在最大限度地保留原始数据信息的同时用尽可能小的子空间对数据聚类。在研究了现有的子空间聚类的基础上,引入了一种新的子空间的搜索方式,它结合簇类大小和信息熵计算子空间维的权重,进一步用子空间的特征向量计算簇类的相似度。该算法采用类似层次聚类中凝聚层次聚类的思想进行聚类,克服了单用信息熵或传统相似度的缺点。通过在Zoo、Votes、Soybean三个典型分类型数据集上进行测试发现：与其他算法相比,该算法不仅提高了聚类精度,而且具有很高的稳定性。     

免疫规划＋K均值混合聚类算法

1.引言聚类分析(Clustering Analysis)是一种无监督的模式识别方法。聚类产生的每一组数据称为一个簇,簇中的每一数据称为一个对象。聚类的目的是使同一簇中对象的特性尽可能地相似,而不同簇对象间的特性差异尽可能地大。聚类的任务是把一个未标记的模式按某种准则划分成若干子集,要求相似的样本尽量归为同一类,而不相似的样本归为不同的类,故又称无监督分类。目前,各种聚类方法已广泛应用于数据挖     

基于实体数据模型的空间邻接定量分析探讨*

空间邻接是一种基本的空间拓扑关系, 它对研究地理现象的分布规律和演化过程有着重要的意义。而目前许多GIS 软件所采用的空间实体数据模型并不包含拓扑信息, 故难以定量地计算、分析地理对象间的空间邻接关系。针对上述缺陷提出了基于实体数据模型的空间邻接定量分析算法, 并以MapInfo 为例予以实现, 最后以实例分析验证算法的有效性。     

搜索路网空间中时空相似轨迹

为了分析移动对象行为特征,需要一种度量轨迹间相似性的方法,虽然在欧氏空间检索移动对象相似轨迹的研究较多,但在路网空间这种研究还不多见。在实际应用方面,大多数移动对象位于路网空间而不是欧氏空间。本文研究了路网空间相似轨迹的特性,并提出了一种在路网空间搜索相似轨迹的度量方法。实验结果表明该方法不仅是搜索相似轨迹的实用技术,也是一种较好的轨迹聚类方法     

基于邻接关系的空间趋势检测算法研究

A regular change of one or more non-spatial attributes can be detected when moving away from a given start object. Moreover, spatial objects are often influenced by their neighbors. And the influence typically decreases or increases more or less continuously with increasing or decreasing distance. Due to the attributes of the neighbors are always similar or associated to each other, spatial trend detection based on spatial neighborhood relations is analyzed to extract useful knowledge in this paper.     

Top-k集合空间关键字近似查询方法

近年来,带有位置和文本信息的空间-文本数据的规模迅速增长,以空间-文本数据为背景的空间关键字查询技术得到广泛的研究与应用。现有大多数空间关键字查询方法通常以单个空间对象作为查询结果的基本单元,最近有少数研究工作提出以一组空间对象作为查询结果的基本单元,这组空间对象联合满足用户的查询需求,但却没有考虑组内空间对象之间的关联关系。针对上述问题,提出一种top-[k]集合空间关键字近似查询方法。提出一种基于关联规则的空间对象之间的关联访问度评估方法,设计了一种结合距离和组内空间对象关联访问度的评分函数;提出了一种基于VP-Tree的剪枝策略,用于快速搜索空间对象的局部邻域,进而加快查询匹配速度;利用评分函数计算候选空间对象组合的得分,并以此选取top-[k]组空间对象作为查询结果。实验结果表明,提出的空间对象关联度评估方法具有较高的准确性,提出的剪枝策略具有较高的执行效率,获取的top-[k]组空间对象具有较高的用户满意度。     

Alternative Solutions for Continuous K Nearest Neighbor Queries in Spatial Network Databases

Continuous K nearest neighbor queries (C-KNN) are defined as finding the nearest points of interest along an enitre path (e.g., finding the three nearest gas stations to a moving car on any point of a pre-specified path). The result of this type of query is a set of intervals (or split points) and their corresponding KNNs, such that the KNNs of all points within each interval are the same. The current studies on C-KNN focus on vector spaces where the distance between two objects is a function of their spatial attributes (e.g., Euclidean distance metric). These studies are not applicable to spatial network databases (SNDB) where the distance between two objects is a function of the network connectivity (e.g., shortest path between two objects). In this paper, we propose two techniques to address C-KNN queries in SNDB: Intersection Examination (IE) and Upper Bound Algorithm (UBA). With IE, we first find the KNNs of all nodes on a path and then, for those adjacent nodes whose nearest neighbors are different, we find the intermediate split points. Finally, we compute the KNNs of the split points using the KNNs of the surrounding nodes. The intuition behind UBA is that the performance of IE can be improved by determining the adjacent nodes that cannot have any split points in between, and consequently eliminating the computation of KNN queries for those nodes. Our empirical experiments show that the UBA approach outperforms IE, specially when the points of interest are sparsely distributed in the network.     

On Detecting Spatial Outliers

The ever-increasing volume of spatial data has greatly challenged our ability to extract useful but implicit knowledge from them. As an important branch of spatial data mining, spatial outlier detection aims to discover the objects whose non-spatial attribute values are significantly different from the values of their spatial neighbors. These objects, called spatial outliers, may reveal important phenomena in a number of applications including traffic control, satellite image analysis, weather forecast, and medical diagnosis. Most of the existing spatial outlier detection algorithms mainly focus on identifying single attribute outliers and could potentially misclassify normal objects as outliers when their neighborhoods contain real spatial outliers with very large or small attribute values. In addition, many spatial applications contain multiple non-spatial attributes which should be processed altogether to identify outliers. To address these two issues, we formulate the spatial outlier detection problem in a general way, design two robust detection algorithms, one for single attribute and the other for multiple attributes, and analyze their computational complexities. Experiments were conducted on a real-world data set, West Nile virus data, to validate the effectiveness of the proposed algorithms.

Improving DBSCAN’s execution time by using a pruning technique on bit vectors

Clustering is the process of assigning a set of physical or abstract objects into previously unknown groups. The goal of clustering is to group similar objects into the same clusters and dissimilar objects into different clusters. Similarities between objects are evaluated by using the attribute values of objects. There are many clustering algorithms in the literature; among them, DBSCAN is a well known density-based clustering algorithm. We improve DBSCAN’s execution time performance for binary data sets and Hamming distances. We achieve considerable speed gains by using a novel pruning technique, as well as bit vectors, and binary operations. Our novel method effectively discards distant neighbors of an object and computes only the distances between an object and its possible neighbors. By discarding distant neighbors, we avoid unnecessary distance computations and use less CPU time when compared with the conventional DBSCAN algorithm. However, the accuracy of our method is identical to that of the original DBSCAN. Experimental test results on real and synthetic data sets demonstrate that, by using our pruning technique, we obtain considerably faster execution time results compared to DBSCAN.     

Solution of parallel computation problem based on “space-time” concept

Construction of distributed systems by way of composition of program objects is considered. It is proposed to define topology of links between the objects by describing a ??neighborhood?? of each object in the form of a list of ??formal neighbors.?? Synchronization of evolution of the object and its neighbors is described in terms of ??local time?? of the object and its neighborhood. Results of solution of real problems on a supercomputer are presented. They demonstrate that it is possible to the reduce labor input required for the creation of distributed software systems to that of local programming.     

一种基于空间相依性的移动对象聚类算法

移动对象的聚类算法,要求能够适应移动对象移动模式动态变化的特点.针对该问题,提出了一种基于空间相依性的移动对象聚类算法.该算法首先计算移动对象之间的空间相依度,空间相依度考虑了移动对象之间的移动速度、方向及位置.当用户之间的空间相依度大于某一阈值时,认为对象之间可达,所有相依度可达对象划分为同一个群组,从而实现移动对象聚类.算法采用一段时间内对象的平均速度和方向代替即时速度和方向,能够有效降低重新聚类次数.实验及分析表明,该算法能够体现移动对象的移动特性,对于移动对象的聚类具有较高性能.     

Spatial Plateau Algebra for implementing fuzzy spatial objects in databases and GIS: Spatial plateau data types and operations

Many geographical applications have to deal with spatial objects that reveal an intrinsically vague or fuzzy nature. A spatial object is fuzzy if locations exist that cannot be assigned completely to the object or to its complement. Spatial database systems and Geographical Information Systems (GIS) are currently unable to cope with this kind of data. Based on an available abstract data model of fuzzy spatial data types for fuzzy points, fuzzy lines, and fuzzy regions that leverages fuzzy set theory and fuzzy point set topology, this article proposes a Spatial Plateau Algebra that provides spatial plateau data types as an implementation of fuzzy spatial data types. Each spatial plateau object consists of a finite number of crisp counterparts that are all adjacent or disjoint to each other, are associated with different membership values, and hence form different plateaus. The formal framework and the implementation are based on well known, exact models and implementations of crisp spatial data types. Spatial plateau operations as geometric operations on spatial plateau objects are expressed as a combination of geometric operations on the underlying crisp spatial objects. This article offers a conceptually clean foundation for implementing a database extension for fuzzy spatial objects and their operations, and demonstrates the embedding of these new data types as attribute data types in a database schema as well as the incorporation of fuzzy spatial operations into a database query language.     

Qualitative Spatial Reasoning with Conceptual Neighborhoods for Agent Control

Research on qualitative spatial reasoning has produced a variety of calculi for reasoning about orientation or direction relations. Such qualitative abstractions are very helpful for agent control and communication between robots and humans. Conceptual neighborhood has been introduced as a means of describing possible changes of spatial relations which e.g. allows action planning at a high level of abstraction. We discuss how the concrete neighborhood structure depends on application-specific parameters and derive corresponding neighborhood structures for the calculus. We demonstrate that conceptual neighborhoods allow resolution of conflicting information by model-based relaxation of spatial constraints. In addition, we address the problem of automatically deriving neighborhood structures and show how this can be achieved if the relations of a calculus can be modeled in another calculus for which the neighborhood structure is known.     

Efficient Evaluation of Probabilistic Advanced Spatial Queries on Existentially Uncertain Data

We study the problem of answering spatial queries in databases where objects exist with some uncertainty and they are associated with an existential probability. The goal of a thresholding probabilistic spatial query is to retrieve the objects that qualify the spatial predicates with probability that exceeds a threshold. Accordingly, a ranking probabilistic spatial query selects the objects with the highest probabilities to qualify the spatial predicates. We propose adaptations of spatial access methods and search algorithms for probabilistic versions of range queries, nearest neighbors, spatial skylines, and reverse nearest neighbors and conduct an extensive experimental study, which evaluates the effectiveness of proposed solutions.