基于半结构化数据模型的频繁模式挖掘研究

为了有效快速地得到频繁模式,通过公共路径舍弃中间重新构造模式树的步骤,充分利用层次结构和深度优先遍历,提出了直接从待挖掘原始半结构化数据中挖掘频繁模式的算法。     

配电网中大数据的挖掘应用

为了适应日益增长的电力需求,解决配电网点多面广、设备多、管理难度大、配电网信息实时性低等问题,本文系统梳理了国际、国内配电网发展现状,以及数据挖掘与分析处理技术在配电网的应用研究情况,将大数据挖掘应用引入配电网日常管理,利用大数据分析,针对电网运行和设备检测或监测数据、电力企业营销数据、交易电价、售电量、用电客户等方面的数据,结合地域配电网网架结构,对配电网存在的薄弱点,尤其在迎峰度夏和春节保供电等用电高峰期,通过事前提前研判、事中实时指挥、事后深入分析,提前发现未来病态设备,推进配电网设备在线化、透明化、智能化,将配电网由“修得快”向“不停电”转变,提升优质服务水平,并希望能对今后相关研究时间工作提供一定的参考价值。     

A regression-based approach for mining user movement patterns from random sample data

Mobile computing systems usually express a user movement trajectory as a sequence of areas that capture the user movement trace. Given a set of user movement trajectories, user movement patterns refer to the sequences of areas through which a user frequently travels. In an attempt to obtain user movement patterns for mobile applications, prior studies explore the problem of mining user movement patterns from the movement logs of mobile users. These movement logs generate a data record whenever a mobile user crosses base station coverage areas. However, this type of movement log does not exist in the system and thus generates extra overheads. By exploiting an existing log, namely, call detail records, this article proposes a Regression-based approach for mining User Movement Patterns (abbreviated as RUMP). This approach views call detail records as random sample trajectory data, and thus, user movement patterns are represented as movement functions in this article. We propose algorithm LS (standing for Large Sequence) to extract the call detail records that capture frequent user movement behaviors. By exploring the spatio-temporal locality of continuous movements (i.e., a mobile user is likely to be in nearby areas if the time interval between consecutive calls is small), we develop algorithm TC (standing for Time Clustering) to cluster call detail records. Then, by utilizing regression analysis, we develop algorithm MF (standing for Movement Function) to derive movement functions. Experimental studies involving both synthetic and real datasets show that RUMP is able to derive user movement functions close to the frequent movement behaviors of mobile users.     

Top-down mining of frequent closed patterns from very high dimensional data

Frequent pattern mining is an essential theme in data mining. Existing algorithms usually use a bottom-up search strategy. However, for very high dimensional data, this strategy cannot fully utilize the minimum support constraint to prune the rowset search space. In this paper, we propose a new method called top-down mining together with a novel row enumeration tree to make full use of the pruning power of the minimum support constraint. Furthermore, to efficiently check if a rowset is closed, we develop a method called the trace-based method. Based on these methods, an algorithm called TD-Close is designed for mining a complete set of frequent closed patterns. To enhance its performance further, we improve it by using new pruning strategies and new data structures that lead to a new algorithm TTD-Close. Our performance study shows that the top-down strategy is effective in cutting down search space and saving memory space, while the trace-based method facilitates the closeness-checking. As a result, the algorithm TTD-Close outperforms the bottom-up search algorithms such as Carpenter and FPclose in most cases. It also runs faster than TD-Close.     

城市地下管网GIS中的空间数据挖掘模型设计

GIS在城市地下管网中的广泛应用导致了管网数据库中数据量的急剧增长。为提高城市地下管网的管理水平,更好地从管网数据库中采集有价值的知识,将空间数据挖掘技术运用到地下管网GIS系统是一种行之有效的方法。介绍GIS和空间数据挖掘结合的必要性,分析了基于空间数据仓库实施空间数据挖掘的理论依据,给出了一个实用的城市地下管网GIS空间数据挖掘模型。该模型可有效地在管网GIS中实现挖掘功能,使GIS具备一定的智能化特征。     

基于元学习的分布式挖掘频繁闭合模式算法研究*

利用元学习技术提出了一种分布式挖掘频繁闭合模式算法;为适应不同的分布式环境,还给出了该算法的一个变种;最后通过实验讨论了不同分布式下选取算法的策略。算法具有挖掘效率高、通信量少、可靠性高的特点,适合分布式挖掘。     

序列模式增量式更新的研究

序列模式挖掘是数据挖掘领域中十分重要的研究课题.目前已有许多算法用于序列模式的挖掘,但在序列模式增量式更新方面的研究还比较少,针对这种情况提出了序列模式增量式更新的挖掘算法SPIU.SPIU算法充分利用了原有的挖掘结果,并对产生的候选频繁序列进行剪枝,有效地减小了候选频繁序列的大小,从而很好地改善了挖掘效率.测试结果表明SPIU算法是正确和高效的,另外算法还具有很好的扩放性.     

FARICS: a method of mining spatial association rules and collocations using clustering and Delaunay diagrams

The paper presents problems pertaining to spatial data mining. Based on the existing solutions a new method of knowledge extraction in the form of spatial association rules and collocations has been worked out and is proposed herein. Delaunay diagram is used for determining neighborhoods. Based on the neighborhood notion, spatial association rules and collocations are defined. A novel algorithm for finding spatial rules and collocations has been presented. The approach allows eliminating the parameters defining neighborhood of objects, thus avoiding multiple “test and trial” repetitions of the process of mining for various parameter values. The presented method has been implemented and tested. The results of the experiments have been discussed.     

面向数字城市多源数据的空间数据立方体数据组织研究

定义了面向数字城市多源数据的空间数据立方体地理空间维、专题维和时间维分别包含的数据种类和内容;设计了它们的维和维层次数据结构;表述了地理空间维、专题维和时间维在概念层次上和物理层次上构成空间数据立方体的方法;确定了地理空间维、专题维和时间维数据的多维数组组织方法,以及多维数据的数据文件和虚拟内存存储策略;表达了多维数组中记录间的关联运算和多维数组的压缩方法。     

A mathematical morphology based method for hierarchical clustering analysis of spatial points on street networks

Spatial clustering analysis is an important issue that has been widely studied to extract the meaningful subgroups of geo-referenced data. Although many approaches have been developed in the literature, efficiently modeling the network constraint that objects (e.g. urban facility) are observed on or alongside a street network remains a challenging task for spatial clustering. Based on the techniques of mathematical morphology, this paper presents a new spatial clustering approach NMMSC designed for mining the grouping patterns of network-constrained point objects. NMMSC is essentially a hierarchical clustering approach, and it generally consists of two main steps: first, the original vector data is converted to raster data by utilizing basic linear unit of network as the pixel in network space; second, based on the specified 1-dimensional raster structure, an extended mathematical morphology operator (i.e. dilation) is iteratively performed to identify spatial point agglomerations with hierarchical structure snapped on a network. Compared to existing methods of network-constrained hierarchical clustering, our method is more efficient for cluster similarity computation with linear time complexity. The effectiveness and efficiency of our approach are verified through the experiments with real and synthetic data sets.     

矢量空间数据库中关联规则的挖掘算法研究

按照矢量空间数据的特点和空间数据挖掘的要求,以GIS的空间分析和空间数据处理为工具,探讨了矢量空间数据库中关联规则挖掘的数据处理方法,提出了关联规则的挖掘算法。最后,通过实例进行了验证。     

Structure and spatial consistency of network-based space layouts for building and product design

Network-based space layouts are schematic models of whole spaces, subspaces, and related physical elements. They address diverse space modeling needs in building and product design. A schema (data model) for network-based space layouts is defined that is influenced by existing space schemas. Layout elements and selected spatial relations form a geometric network. The network is embedded in 3-space and facilitates analysis with graph and network algorithms. Spatial constraints on layout elements and spatial relations extend the schema to support spatial consistency checking. Spatially consistent layouts are required for reliable network analysis and desirable for layout modification operations. An operation is introduced that evaluates spatial constraints to detect and semi- or fully-automatically resolve spatial inconsistencies in a layout. A layout modeling system prototype that includes proof-of-concept implementations of the layout schema extended by spatial constraints and the inconsistency resolution operation is described. Layouts of a floor of an office building and a rack server cabinet have been modeled with the system prototype.