大规模数据库中的知识获取

一、前言数据是知识的潭泉,拥有大量的数据与拥有许多有用的知识完全是两回事.为了有效地利用大量的公共数据,必须更好地理解这些数据,并从其中快速、准确地发现知识.这里所说的知识是指大量数据中存在的规律性(r egularity)或不同属性值之间所存在的[I F THEN〕规则.将所获取的知识附加于仅由事实数据(fact data)构成的传统数据库上,既可强化数据库的查询能力,又可给数据库提供推理能力,     

查询与发现数据挖掘技术分析

探讨了数据库应用重要技术－数据挖掘技术，描述了数据查询的结构，已有的各种方式及常用技术，涵盖了大部分流行工具，比如分类、聚类、概括，总结了目前使用的统计与机器学习方法和新的解决方案。     

基于Hilbert空间理论的图像知识发现

最近几年，知识发现研究的进展很快。目前，在知识发现领域图像数据知识发现形成了新的研究热点。本文介绍了基于Hilbert空间理论的图像知识发现模型IMDFSSM，采用模式（定义为Hilbert空间中的矢量）来定量地表征图像数据的知识表示和参与知识发现过程。然后用图像挖掘系统作为实例进行了验证，结果表明该模型对于图像数据的知识发现过程具有指导性作用。     

Knowledge Discovery Through Self-Organizing Maps: Data Visualization and Query Processing

In data mining, the usefulness of a data pattern depends on the user of the database and does not solely depend on the statistical strength of the pattern. Based on the premise that heuristic search in combinatorial spaces built on computer and human cognitive theories is useful for effective knowledge discovery, this study investigates how the use of self-organizing maps as a tool of data visualization in data mining plays a significant role in human–computer interactive knowledge discovery. This article presents the conceptual foundations of the integration of data visualization and query processing for knowledge discovery, and proposes a set of query functions for the validation of self-organizing maps in data mining. Received 1 November 1999 / Revised 2 March 2000 / Accepted in revised form 20 October 2000     

A Knowledge Discovery Framework for Civil Infrastructure: A Case Study of the Intelligent Workplace

Large databases are becoming increasingly common in civil infrastructure applications. Although it is relatively simple to specifically query these databases at a low level, more abstract questions like ‘How does the environment affect pavement cracking?’ are difficult to answer with traditional methods. Data mining techniques can provide a solution for learning abstract knowledge from civil infrastruc-ture databases. However, data mining needs to be performed within a systematic process to ensure correct and reproducible results. Many decisions must be made during this process, making it difficult for novice analysts to apply data mining techniques thoroughly. This paper presents an application of a knowledge discovery process to data collected for an ‘intelligent’ building. The knowledge discovery process is illustrated and explained through this case study. Additionally, we discuss the importance of this case study in the context of a research effort to develop an interactive guide for the knowledge discovery process.     

基于数据挖掘的入侵检测研究

网络入侵检测系统已经成为网络安全架构的一部分.但是当前的NIDS（network intrusion detection system）在未知攻击的检测上都存在虚警率过高的问题.首先对在线和离线系统的优缺点做了对比,重点介绍了分类器的集成学习和多检测器关联以及数据挖掘方法中的一些实用技术,然后介绍现存的系统和评价数据集,最后总结了入侵检测领域的工作并给出了这个领域的发展方向.     

基于知识发现的实用智能系统总体结构模型

知识发现系统与实用智能系统的有机融合成为信息管理、辅助决策支持、智能预测支持等领域亟待解决的关键问题之一。该文将以系统的观点和方法，从哲学方法论的角度来审视和研究基于知识发现的实用智能系统(如专家系统、决策支持系统和预测系统)的总体结构模型，并在此基础上为形成统一的、完备的和具有认知自主性的实用智能系统提供指导依据。     

模糊聚类分析在KDD中的应用研究

应用模糊聚类算法确定关键条件属性集，探索教师教学质量评估数据库中评估等级同评估项目之间的规则知识。     

一个通用型知识发现系统中数据预处理的实现

介绍了一个通用型知识发现系统的数据预处理部分的主要设计思想和实现方法。该系统的数据预处理部分运用了视图机制、主题管理、语言场理论等思想方法,对基于各种关系型数据库的知识发现工具的实现进行了有益的尝试,并对未来的工作做了展望。     

利用基因遗传算法从数据库自动生成知识库

此文提出一种从数据库自动生成知识库的新方法。该方法从数据库到知识库的优化目标函数，利用基因遗传算法的优化手段，直接从数据库中生成性能较优的知识库。文中论述了该方法的原理与具体实现过程。     

DCS故障诊断专家系统中知识自动获取的研究

着重叙述了知识自动获取系统，其与目前最常用的开发工具不同，在设计与开发中采用了最新的数据库软件来作为开发工具，同时对新系统中知识输入的步骤、知识输入界面及知识输方法作了介绍。     

基于知识发现的范例推理系统

1 引言范例推理(Case-Based Reasoning,CBR)是近十几年来人工智能中发展起来的区别于基于规则推理的一种推理模式,它是指借用旧的事例或经验来解决问题、评价解决方案、解释异常情况或理解新情况。CBR兴起的主要原因是传统的基于规则的系统存在诸多的缺点,如:在知识获取问题上存在困难,对于处理过的问题没有记忆而导致推理效率低下,不能有效地处理例外情况,整体性能较为脆弱等等,而CBR恰好能解决以上问题。     

Knowledge Discovery for A Temporal Prediction Rules

We are obtaining a large database of some objects' records of fluctuations of a stock market,medical treatments,changes of weather in certain area and so on,where each record consists of multi-attributes taking multi-values changing with time. Our work is motivated by prediction,which is different from the work in 4,5,8,11. We want to help learn from past data and make informed decisions for the future. This paper is very significant to perfect the theory and the development of the temporal data mining.     

一种基于遗传算法的知识挖掘算法

数据挖掘是近年来数据库领域中出现的一个新兴研究热点。传统的数据挖掘是从大量数据中获取知识。该文基于遗传算法提出了一种新的知识获取方法－ＧＡＫＤＫ,通过对知识树的交叉交异操作,从现有规则中获取未发现的知识。     

基于属性分类和回归模式的知识发现研究

该文针对目前数据挖掘的研究状况,理论上提出了将基于属性分类方法和多元线形回归算法相结合的算法,首先使用基于属性分类的方法将原始数据库进行属性分类,化简,去掉次要的条件属性,最后得出一个简化的表格,找出影响决策属性的主要因素,根据此表,可以得出简单的ifthen规则;然后使用多元线形回归求出它们之间的近似定量关系,得出一个最优回归方程。     

Three perspectives of data mining

This paper reviews three recent books on data mining written from three different perspectives, i.e., databases, machine learning, and statistics. Although the exploration in this paper is suggestive instead of conclusive, it reveals that besides some common properties, different perspectives lay strong emphases on different aspects of data mining. The emphasis of the database perspective is on efficiency because this perspective strongly concerns the whole discovery process and huge data volume. The emphasis of the machine learning perspective is on effectiveness because this perspective is heavily attracted by substantive heuristics working well in data analysis although they may not always be useful. As for the statistics perspective, its emphasis is on validity because this perspective cares much for mathematical soundness behind mining methods.     

面向应用领域的知识发现系统开发平台KDIST

介绍了一个面向应用领域的知识发现系统开发平台KDIST。将数据挖掘技术巧妙地封装在应用领域的问题中,使开发出的知识发现系统操作傻瓜化,用户无须关心数据挖掘本身,有效地减轻了领域用户使用负担,提高了数据挖掘技术实用性。所开发出的知识发现系统将挖掘得到的知识融合到已有专家系统的知识库中。两个实例系统的应用证明知识发现系统是专家系统自动半自动知识获取和知识库精化的良好工具。     

数据采掘与知识发现综述

来自应用、社会、经济等各方面的迫切需求，以及不断升温的研究兴趣，使知识发现和数据采掘成为目前一个不断发展的领域。本文介绍了知识发现和数据采掘技术的产生背景、基本任务、方法及其应用，同时还简要介绍了目前已有的成熟的KDD系统及其将来的发展。     

MODL: A Bayes optimal discretization method for continuous attributes

While real data often comes in mixed format, discrete and continuous, many supervised induction algorithms require discrete data. Efficient discretization of continuous attributes is an important problem that has effects on speed, accuracy and understandability of the induction models. In this paper, we propose a new discretization method MODL¹, founded on a Bayesian approach. We introduce a space of discretization models and a prior distribution defined on this model space. This results in the definition of a Bayes optimal evaluation criterion of discretizations. We then propose a new super-linear optimization algorithm that manages to find near-optimal discretizations. Extensive comparative experiments both on real and synthetic data demonstrate the high inductive performances obtained by the new discretization method. Editor: Tom Fawcett ¹French patent No. 04 00179.     

发现分类规则知识的一种算法

提出一种基于信息论与集合论的基本理论相结合的方法,用来从数据库发现分类规则知识;利用该方法可以快速发现知识,且发现的知识简捷、可靠。