无人机发动机综合测试系统的设计与实现

介绍了某大型无人机发动机参数测试系统的组成、工作原理和软硬件设计，重点阐述了系统采用的软硬件结合抗干扰技术和大规模试车数据存储方法。该系统实现了发动机主要试车参数的自动化智能测试。实际应用证明，系统性能稳定可靠，操作简便，显著提高了发动机试车参数的测试精度和工作效率。     

快速开采意外的规则

数据开采研究的主要任务就是发现数据集合中隐含的规则，发现的规则分为常规性规则和意外规则。由于意外规则的支持度很小，因而常常被人们忽略，然而意外规则代表了知识的另外一面，非常有用。文章提出一种快速有效发现意外规则的算法，该算法允许规则的结果为合取范式。     

约束关联规则技术检测液体火箭发动机稳态过程

采用了一种基于约束关联规则的分类技术的故障检测方法,对液体火箭发动机的稳态过程试车数据进行挖掘的应用实例表明,该方法能够有效地挖掘测量参数和液体火箭发动机工作情况之间的关联关系,有效地检测出液体火箭发动机的稳态过程中的故障。     

基于数据融合的知识发现方法在网络管理中的应用

提出用于网络管理的基于数据融合的知识发现系统框架，研究数据融合技术在知识发现的数据准备和预处理阶段的应用，研究关联规则在表达网络管理知识方面的适用性并针对网络管理数据时序性的特点，引入情景规则来表示期望发掘的知识，指出网络故障管理中关联规则和情景规则的挖掘算法以及知识增量式更新的算法，并简介了原型系统的实现方法。     

图像数据挖掘中的关联规则

介绍了遥感图像数据的关联规则挖掘方法。对目前图像数据挖掘的研究及应用现状进行了综述。介绍了图像数据关联规则挖掘的最新进展，总结了图像数据关联规则挖掘的特点、类型及图像数据预处理的技术方法。     

主动引射高空模拟试车台流场瞬态数值模拟

大部分高空模拟试车台的数值模拟(不论是主动引射还是被动引射)仅模拟了发动机稳定运行的稳态流场，然而发动机启动、熄火以及在发动机运行的过程中药形的变化(药柱燃烧速率变化)都会影响发动机室压从而改变试验舱舱压。针对某大型主动引射高空模拟试车台，应用某商业软件模拟了q_m=20 kg/s、q_m=50 kg/s和q_m=200 kg/s这3个典型流量的发动机工作全程中某主动引射高空模拟试车台的流场。选取了几个典型工况进行分析并与试验数据进行对比，仿真数据与试验数据对比良好。通过此研究，在试验前能够较准确地预示引射舱压曲线，揭示舱压规律，指导真实试验，降低试验风险。     

非规则数据场体绘制技术的研究

科学计算可视化的核心是三维数据场的可视化．当前三维可视化的研究热点是体绘制技术。文中介绍了三维非规则数据场体绘制技术的研究现状。在此基础上，通过对已有非规则数据场体绘制技术和算法的分析比较．预测非规则数据场体绘制技术今后的发展趋势以及将来应该重视的研究方向。除了改进已有算法、将各种算法结合起来外，还应该在硬件及系统加速技术方面做研究，同时结合漫游技术研究和开发高效的三维空间非规则数据场的可视化技术和并行算法。     

一种从不完备数据集中发现规则的RS方法

Rough集(Rough SetS、Rs)理论被广泛应刑于数据分类问题，该文用基于RS的方法从不完备数据集中产生确定和可能的规则集，提出了一种新的规则发现算法，可以同时从不完备数据集中产生规则和估计缺失值，并指出了进一步的研究方向。     

一种基于FP树的挖掘关联规则的增量更新算法

挖掘关联规则是数据挖掘领域的一个重要研究方向．人们已经提出了许多用于高效地发现大规模数据库中关联规则的算法，但对关联规则维护问题的研究却比较少．该文在FP树的基础上，引入支持度函数的慨念，对FP树进行改造，提出了一种关于挖掘关联规则的增量更新算法IFP—growth．该算法既考虑了数据集中数据的增加．同时又考虑了数据集中数据的减少等情况下关联规则的维护问题，并且还可以把增量更新的5种情形简化为3种情形．使用本算法来挖掘关联规则可以避免生成大量的候选项目集，而且非常高效．     

基于逻辑数据模型的异构数据交换规则研究

文章在研究与分析异构数据对应规则和异构数据转换规则的基础上，提出了建立在XML和面向对象数据库逻辑数据模型之上的异构数据交换规则。     

基于LabVIEW的航空发动机电子控制器检测系统的开发与应用

通过研究某型航空发动机电子控制器(EEC)的调节程序和接口关系,分析发动机使用中检测的需求,开发了外场在线检测系统.该系统运用LabVIEW软件开发平台,以平板电脑为核心,结合试车数据库,可以完成航空发动机性能曲线的录取和EEC的功能测试.     

数据挖掘技术

数据挖掘技术是当前数据库和人工智能领域研究的热点课题,为了使人们对该领域现状有个概略了解,在消化大量文献资料的基础上,首先对数据挖掘技术的国内外总体研究情况进行了概略介绍,包括数据挖掘技术的产生背景、应用领域、分类及主要挖掘技术;结合作者的研究工作,对关联规则的挖掘、分类规则的挖掘、离群数据的挖掘及聚类分析作了 较详细的论述;介绍了关联规则挖掘的主要研究成果,同时指出了关联规则衡量标准的不足及其改进方法,提出了分类模式的准确度评估方法;最后,描述了数据挖掘技术在科学研究、金属投资、市场营销、保险业、制造业及通信网络管理等行业的应用情况,并对数据挖掘技术的应用前景作了展望。     

Visualization techniques for mining large databases: a comparison

Visual data mining techniques have proven to be of high value in exploratory data analysis, and they also have a high potential for mining large databases. In this article, we describe and evaluate a new visualization-based approach to mining large databases. The basic idea of our visual data mining techniques is to represent as many data items as possible on the screen at the same time by mapping each data value to a pixel of the screen and arranging the pixels adequately. The major goal of this article is to evaluate our visual data mining techniques and to compare them to other well-known visualization techniques for multidimensional data: the parallel coordinate and stick-figure visualization techniques. For the evaluation of visual data mining techniques, the perception of data properties counts most, while the CPU time and the number of secondary storage accesses are only of secondary importance. In addition to testing the visualization techniques using real data, we developed a testing environment for database visualizations similar to the benchmark approach used for comparing the performance of database systems. The testing environment allows the generation of test data sets with predefined data characteristics which are important for comparing the perceptual abilities of visual data mining techniques     

数据挖掘技术初探

数据挖掘技术已成为机器学习、数据库系统、人工智能等领域内热门的研究方向 .本文将讨论数据挖掘的基本概念 ,并在此基础上介绍、分析挖掘关联规则技术、决策树、聚类分析、数据管道等常用数据挖掘技术     

A privacy-preserving technique for Euclidean distance-based mining algorithms using Fourier-related transforms

Privacy preserving data mining has become increasingly popular because it allows sharing of privacy-sensitive data for analysis purposes. However, existing techniques such as random perturbation do not fare well for simple yet widely used and efficient Euclidean distance-based mining algorithms. Although original data distributions can be pretty accurately reconstructed from the perturbed data, distances between individual data points are not preserved, leading to poor accuracy for the distance-based mining methods. Besides, they do not generally focus on data reduction. Other studies on secure multi-party computation often concentrate on techniques useful to very specific mining algorithms and scenarios such that they require modification of the mining algorithms and are often difficult to generalize to other mining algorithms or scenarios. This paper proposes a novel generalized approach using the well-known energy compaction power of Fourier-related transforms to hide sensitive data values and to approximately preserve Euclidean distances in centralized and distributed scenarios to a great degree of accuracy. Three algorithms to select the most important transform coefficients are presented, one for a centralized database case, the second one for a horizontally partitioned, and the third one for a vertically partitioned database case. Experimental results demonstrate the effectiveness of the proposed approach.     

我国水文数据挖掘技术研究的回顾与展望

水文科学研究的领域面临来自许多方面的不确定性和非确知问题。引入数据挖掘的理论与技术,结合水文科学发展的需要,充分应用以计算机技术为基础的现代信息技术,研究水文数据挖掘的理论、技术和方法,为解决水文科学研究面临的问题提供了新的思路。当前,水文数据挖掘研究还处于起步阶段,研究内容多集中在水文数据的单项和局部数据的模拟与处理方面,对基于水文数据库的全局性多因素数据挖掘涉及很少,在数据挖掘技术与水文数据适应性方面所进行的研究也还很不够。为了充分发挥数据挖掘发现知识的作用,需要在水文主题数据库和多维数据立方、水文序列的分类、聚类和关联规则挖掘技术及优化算法以及水文序列的相似性、周期性和其它序列模式挖掘方面开展进一步研究,并向形成水文数据挖掘软件及数据平台方向发展。     

空间数据挖掘发展研究

空间数据挖掘是指对空间数据库中非显式存在的知识、空间关系或其它有意义的模式等的提取,在地理信息系统、地理市场、遥感、图像数据勘测、医学图像处理、导航、交通控制、环境研究等各种领域有着广泛的应用。该文从空间数据挖掘的定义、过程、特征和任务等方面对空间数据挖掘技术进行了研究,并介绍了一个空间数据挖掘原型—GeoMiner和未来的研究方向。     

决策树算法的并行性研究

在数据库和数据仓库中运用数据挖掘技术必须考虑挖掘系统的速度问题。当数据集大到相当程度时，挖掘工作只能在巨型机上进行；而由于系统的速度不够快，挖掘出来的知识将会是滞后的，它对决策支持不仅无效甚至是有害的。针对这一问题，提出了决策树算法的并行机制，并对并行性的性能进行探讨。     

Positive and negative association rule mining on XML data streams in database as a service concept

In recent years, data mining has become one of the most popular techniques for data owners to determine their strategies. Association rule mining is a data mining approach that is used widely in traditional databases and usually to find the positive association rules. However, there are some other challenging rule mining topics like data stream mining and negative association rule mining. Besides, organizations want to concentrate on their own business and outsource the rest of their work. This approach is named “database as a service concept” and provides lots of benefits to data owner, but, at the same time, brings out some security problems. In this paper, a rule mining system has been proposed that provides efficient and secure solution to positive and negative association rule computation on XML data streams in database as a service concept. The system is implemented and several experiments have been done with different synthetic data sets to show the performance and efficiency of the proposed system.     

Application of data mining techniques in customer relationship management: A literature review and classification

Despite the importance of data mining techniques to customer relationship management (CRM), there is a lack of a comprehensive literature review and a classification scheme for it. This is the first identifiable academic literature review of the application of data mining techniques to CRM. It provides an academic database of literature between the period of 2000–2006 covering 24 journals and proposes a classification scheme to classify the articles. Nine hundred articles were identified and reviewed for their direct relevance to applying data mining techniques to CRM. Eighty-seven articles were subsequently selected, reviewed and classified. Each of the 87 selected papers was categorized on four CRM dimensions (Customer Identification, Customer Attraction, Customer Retention and Customer Development) and seven data mining functions (Association, Classification, Clustering, Forecasting, Regression, Sequence Discovery and Visualization). Papers were further classified into nine sub-categories of CRM elements under different data mining techniques based on the major focus of each paper. The review and classification process was independently verified. Findings of this paper indicate that the research area of customer retention received most research attention. Of these, most are related to one-to-one marketing and loyalty programs respectively. On the other hand, classification and association models are the two commonly used models for data mining in CRM. Our analysis provides a roadmap to guide future research and facilitate knowledge accumulation and creation concerning the application of data mining techniques in CRM.