Federated Searching

ABSTRACT

With an increasing number of libraries implementing federated searching to help users negotiate the complex electronic database environment, it is important to note how users are being instructed to use these resources. This article presents the results of examinations of the Web sites of Association of Research Libraries (ARL) member institutions that currently use federated search products. It analyzes help guides and search tips related to federated searching to determine what concepts are being explained to users and how the information is being communicated. The result of the evaluation of these Web sites is a list of “best practice” guidelines that may be used by librarians creating their own federated searching guides. doi:10.1300/J136v12n03_03     

基于关联规则挖掘的课程设置合理性分析

利用数据挖掘的方法挖掘出课程之间的相关性,再根据这个相关性去分析目前的课程设置是否合理,这样可以更科学地制定教学计划从而达到提高教学质量和学生学习效果的目的.     

一种基于组合方式改进的频繁项集挖掘算法

FP-growth算法用于关联规则挖掘分成两个阶段：构建频繁模式树和进行频繁模式挖掘;对这两个阶段分别进行改进,若项头表中存在同频度的频繁项,在构建FP-tree的过程动态调整其位置,构建压缩的最优化FP-tree,提出了IMFP-tree算法。在进行频繁模式挖掘阶段,提出CFP-mine算法,CFP-mine算法采用一种新方法构建条件模式基,且采用组合方式挖掘频繁项集,有别于传统FP-growth算法的挖掘过程,理论上证明和实验验证本算法的正确性和高效性。     

Region-Based Hierarchical Image Matching

This paper presents an approach to region-based hierarchical image matching, where, given two images, the goal is to identify the largest part in image 1 and its match in image 2 having the maximum similarity measure defined in terms of geometric and photometric properties of regions (e.g., area, boundary shape, and color), as well as region topology (e.g., recursive embedding of regions). To this end, each image is represented by a tree of recursively embedded regions, obtained by a multiscale segmentation algorithm. This allows us to pose image matching as the tree matching problem. To overcome imaging noise, one-to-one, many-to-one, and many-to-many node correspondences are allowed. The trees are first augmented with new nodes generated by merging adjacent sibling nodes, which produces directed acyclic graphs (DAGs). Then, transitive closures of the DAGs are constructed, and the tree matching problem reformulated as finding a bijection between the two transitive closures on DAGs, while preserving the connectivity and ancestor-descendant relationships of the original trees. The proposed approach is validated on real images showing similar objects, captured under different types of noise, including differences in lighting conditions, scales, or viewpoints, amidst limited occlusion and clutter.     

基于J2EE的O/R(Object-Relation)映射方案研究

对象模型和关系模型是两种完全不同的模式,在开发中如何实现对象到关系的映射是一个关键的问题,分析了对象关系映射不匹配的各个方面,以及当前的各种持久化解决方案,并重点分析了透明对象关系映射的特点,最后对如何实现对象关系映射的关键技术进行了研究.     

基于完全加权关联规则挖掘和查询扩展的信息检索

将完全加权关联规则挖掘技术应用于信息检索,提出一种基于完全加权关联规则挖掘和查询扩展的信息检索模型,重点阐述模型的设计思想及其算法和实现的关键技术.实验结果表明,该模型有效,能提高信息检索性能,有很高的实际应用价值和推广前景.     

基于关联规则的分布式通信网告警相关性研究

描述了基于数据挖掘的通信网告警相关性分析.在分布式数据库中直接运用序列算法效率很低,因为这需要大量的额外通信.为此提出了一种有效的分布式关联规则挖掘算法--EDMA,它通过局部剪枝与全局剪枝来最小化候选项集数目和通信量.在局部站点上运用先进的压缩关联矩阵CMatrix统计局部项集支持数.此外还利用项目剪枝与交易剪枝共同来减少扫描时间.最后仿真验证了EDMA比其他经典分布式算法有更高的运算效率、更低的通信开销以及更好的可扩展性.     

基于排序的关联分类算法

提出了一种基于排序的关联分类算法.利用基于规则的分类方法中择优方法偏爱高精度规则的思想和考虑尽可能多的规则,改进了CBA(Classification Based on Associations)只根据少数几条覆盖训练集的规则构造分类器的片面性.首先采用关联规则挖掘算法产生后件为类标号的关联规则,然后根据长度、置信度、支持度和提升度等对规则进行排序,并在排序时删除对分类结果没有影响的规则.排序后的规则加上一个默认分类便构成最终的分类器.选用20个UCI公共数据集的实验结果表明,提出的算法比CBA具有更高的平均分类精度.     

一种基于决策树的快速关联规则挖掘算法

本文对关联规则的挖掘问题进行了深入研究.在总结现有算法优缺点的基础上,提出了一种新的基于决策树的快速关联规则挖掘算法(RABDT),结合决策树的构造过程,给出了算法的原理和实现步骤,并通过实验对比验证了算法的有效性.     

一种基于阈值变化的增量式更新算法研究

关联规则的挖掘算法已被数据库界广泛研究,这些关联规则挖掘算法在原有的基础上不断被优化、改进,使挖掘的效率不断提高.本文讨论了度变小的情况下典型的增量算法IUA算法以及它已有的一些改进,分析了特点与不足,在此基础上,提出了改进算法QIUA算法,并且从理论和实验两个方面验证了算法的可行性与有效性.