主从关系数据库中关联规则挖掘算法研究

数据挖掘是计算机科学研究的重要领域之一.文章从主从关系数据集的角度开展关联规则挖掘研究,首先构建了主从关系数据库模型,在此基础上提出一种基于元组ID逆传输的关联规则挖掘算法(TIDRP),避免了挖掘过程中数据的集成过程,减少了资源的消耗,并使挖掘出的规则更符合实际情况.     

基于字形编码的中文字符密码算法研究

现有文献对于中文字符的加解密研究均是将其转换成GB码或Unicode码后再进行的,并没有出现一种针对中文字符自身特点来分析研究的密码算法.依据中文字符象形特征进行详细分析后,结合字形的分解以及比较各种经典密码算法(例如:凯撒、维吉尼亚、希尔、DES、RSA等)对其的加解密应用,最后通过密码安全分析得出适合中文自身特点的密码算法,为中文密码学的研究提供一个新思路.     

集中供热管理数据挖掘系统

基于数据挖掘技术，以集中供热管理为研究对象，提出了关联数据挖掘的设计思想及实现方法。采用了属性构造法进行数据预处理，建立了数据挖掘模型，实现了关联规则算法，并对挖掘结果进行解释与分析。分析表明：对于供热系统的温度、压力和流量，其热量消耗应满足最小支持度和最小置信度阈值。     

信息系统风险评估的数学方法

给出了现代网络信息系统风险域及其风险点的形式化数学描述，建立了风险估计模型，对不同域中的风险关联性作了探讨，为网络信息系统的安全性评估提供了可行的数学方法。     

Data association based on target signal classification information

In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too close to each other. To enhance the tracking accuracy, the target signal classification information （TSCI） should be used to improve the data association. The TSCI is integrated in the data association process using the JPDA （joint probabilistic data association）. The use of the TSCI in the data association can improve discrimination by yielding a purer track and preserving continuity. To verify the validity of the application of TSCI, two simulation experiments are done on an air target-tracing problem, that is, one using the TSCI and the other not using the TSCI. The final comparison shows that the use of the TSCI can effectively improve tracking accuracy.     

Multiple targets vector miss distance measurement accuracy based on 2-D assignment algorithms

An extension of 2-D assignment approach is proposed for measurement-to-target association for improving multiple targets vector miss distance measurement accuracy. When the multiple targets move so closely, the measurements can not be fully resolved due to finite resolution. The proposed method adopts an auction algorithm to compute the feasible measurement-to-target assignment with unresolved measurements for solving this 2-D assignment problem. Computer simulation results demonstrate the effectiveness and feasibility of this method.     

The human genome and understanding of common disease: present and future technologies

The study of candidate genes over the past three decades has yielded notable successes in common-disease genetics. During this time, however, interpretation of genetic association studies has been hampered by the use of clinical cohorts of inadequate power and insufficient information on genetic variation in candidate genes. The unavailability of highthroughput and low-cost genotyping technologies has also limited the scope of complex-disease genetic studies. More recently, however, the sequencing and characterization of variation within the human genome has revolutionized genetic studies and enabled full genome-wide scans for genes associated with disease. The identification of disease-associated (causative) genes has illuminated disease mechanisms. The translation of this knowledge into direct clinical benefit in diagnosis, prognosis and therapy for an individual’s disease still remains a challenge. Received 11 September 2006; received after revision 17 December 2006; accepted 18 January 2007     

数据库中布尔型及广义模糊型加权关联规则的挖掘

数据挖掘主要是用来找出隐藏在数据库当中那些有用的而未被发现的知识本文我们引入布尔型加权关联规则和广义模糊型加权关联规则的概念,并分别给出挖掘这些规则的计算方法.     

基于概念格提取简洁关联规则

从量化封闭项集格所提取的所有最小无冗余规则,虽满足最小前件最大后件的要求,但并不是满足用户设定支持度和置信度的最小规则集.本文提出了一种全局简洁关联规则,使所提取的规则集最小,并给出了基于量化封闭项集格提取全局简洁规则的算法.     

一种基于关联规则的搜索引擎结果聚类算法

提出一种搜索引擎结果的聚类方法,采用关联规则方法确定网页文档中的显著短语,并把这些显著短语作为对相应聚类的描述,聚类中包含的文档就是显著短语所关联的文档,然后对形成的聚类按照相关度分数进行排队,最终把结果展现给用户.