ESTIMATING DBLEARN'S POTENTIAL FOR KNOWLEDGE DISCOVERY IN DATABASES

We propose a procedure for estimating DBLEARN's potential for knowledge discovery, given a relational database and concept hierarchies. This procedure is most useful for evaluating alternative concept hierarchies for the same database. The DBLEARN knowledge discovery program uses an attribute-oriented inductive-inference method to discover potentially significant high-level relationships in a database. A concept forest, with at most one concept hierarchy for each attribute, defines the possible generalizations that DBLEARN can make for a database. The potential for discovery in a database is estimated by examining the complexity of the corresponding concept forest. Two heuristic measures are defined based on the number, depth, and height of the interior nodes. Higher values for these measures indicate more complex concept forests and arguably more potential for discovery. Experimental results using a variety of concept forests and four commercial databases show that in practice both measures permit quite reliable decisions to be made; thus, the simplest may be most appropriate.     

Feature Generation Using General Constructor Functions

Most classification algorithms receive as input a set of attributes of the classified objects. In many cases, however, the supplied set of attributes is not sufficient for creating an accurate, succinct and comprehensible representation of the target concept. To overcome this problem, researchers have proposed algorithms for automatic construction of features. The majority of these algorithms use a limited predefined set of operators for building new features. In this paper we propose a generalized and flexible framework that is capable of generating features from any given set of constructor functions. These can be domain-independent functions such as arithmetic and logic operators, or domain-dependent operators that rely on partial knowledge on the part of the user. The paper describes an algorithm which receives as input a set of classified objects, a set of attributes, and a specification for a set of constructor functions that contains their domains, ranges and properties. The algorithm produces as output a set of generated features that can be used by standard concept learners to create improved classifiers. The algorithm maintains a set of its best generated features and improves this set iteratively. During each iteration, the algorithm performs a beam search over its defined feature space and constructs new features by applying constructor functions to the members of its current feature set. The search is guided by general heuristic measures that are not confined to a specific feature representation. The algorithm was applied to a variety of classification problems and was able to generate features that were strongly related to the underlying target concepts. These features also significantly improved the accuracy achieved by standard concept learners, for a variety of classification problems.     

半结构化、层次数据的模式发现

Web数据资源及数据集成引发了半结构化数据问题,半结构化数据指其结构隐含或不规整的自描述数据。由于缺乏独立于数据的模式,有效地查询划浏览该类数据比较困难,半结构化数据的模式发现成为解决该问题的基础步骤。本文提出的算法能够快速有效地发现半结构化层次数据中的规整结构。它采用自顶向下的生成,结合有效的剪枝策略,从OEM模型表达的半结构化层次数据中构建模式树。     

Discovery as Autonomous Learning from the Environment

Discovery involves collaboration among many intelligent activities. However, little is known about how and in what form such collaboration occurs. In this article, a framework is proposed for autonomous systems that learn and discover from their environment. Within this framework, many intelligent activities such as perception, action, exploration, experimentation, learning, problem solving, and new term construction can be integrated in a coherent way. The framework is presented in detail through an implemented system called LIVE, and is evaluated through the performance of LIVE on several discovery tasks. The conclusion is that autonomous learning from the environment is a feasible approach for integrating the activities involved in a discovery process.     

物理网络拓扑发现算法的研究

局域网交换技术提高了局域网内网格带宽使用的有效性并且部分解决了现有网络带宽问题，在对原有基于SNMP MIB－II的物理网络设备发现算法分析比较的基础上，提出了一种新的算法，用该算法开发的拓扑发现工具能准确地发现物理网络拓扑即子网内交换机到交换机，交换机到路由器，交换机到主机之间的连接关系。     

基于知识的快速角点提取

本文提出一种基于查表技术知识的基角点提取方法．就一定的考察点数目来说，该方法由于采用了人的知识，其检测效果是很好的．由于采用了查表技术，该方法的速度非常快，并且易于硬件实现．当用硬件实现该方法，对于５１２×５１２的真实图像，角．点提取能以电视速率进行．本文详细讨论了查找表的构造方法和知识的采集方法．实验表明该方法工作得很好．     

面向三维变量设计的可变特征模型

本文提出了一个面向三维变量设计的产品形状可变特征模型，其中包括特征树，特征表有特征约束关系图等新概念，用于描述设计过程中的形体模型和特征间的相互内在联系，从而不仅可作参数化设计，还支持更广泛意义上的变量设计。     

船舶照明CAD系统的特征建模

本文论述了将特征建模用于计算机辅助船舶照明系统设计的基本原理和方法。通过特征建模和智能辅助，使照明布置图和系统图的自动生成获得了极好的效果。     

OPTIMONOTONE MEASURES FOR OPTIMAL RULE DISCOVERY

Many studies have shown the limits of the support/confidence framework used in Apriori ‐like algorithms to mine association rules. There are a lot of efficient implementations based on the antimonotony property of the support, but candidate set generation (e.g., frequent item set mining) is still costly. In addition, many rules are uninteresting or redundant and one can miss interesting rules like nuggets. We are thus facing a complexity issue and a quality issue. One solution is to not use frequent itemset mining and to focus as soon as possible on interesting rules using additional interestingness measures. We present here a formal framework that allows us to make a link between analytic and algorithmic properties of interestingness measures. We introduce the notion of optimonotony in relation with the optimal rule discovery framework. We then demonstrate a necessary and sufficient condition for the existence of optimonotony. This result can thus be applied to classify the measures. We study the case of 39 classical measures and show that 31 of them are optimonotone. These optimonotone measures can thus be used with an underlying pruning strategy. Empirical evaluations show that the pruning strategy is efficient and leads to the discovery of nuggets using an optimonotone measure and without the support constraint.     

机器发现的递归函数法

为了解决机器发现所面临的问题,本文提出了一种新的机器发现方法——递归函数法,该方法把待求函数本身作为唯一的理论项,从而保证了理论项的合适性;其发现能力也有所提高,该方法采用原始递归式定义函数,递归函数在理论上比较完善,故该方法具有很好的理论依据并且便于计算机实现。     

匿名用户的网络浏览特征挖掘

在网络使用挖掘（web usage mining)中，分析用户的行为模式是一个关键的问题，尤其对于匿名用户特征挖掘更有实际意义，首先介绍如何从网络使用数据（web usage data)中提取出会话（session)信息，接着讨论会话的特征抽取和特征空间（feature space)的表达方式，并以此为基础提出了一种建立在会话特征信息上的戒名用户的网络浏览特征挖掘方法算法，这种算法在提高精确性的基础上减少了计算耗费，可以较好地解决路径的变长，方向性和动态聚集等问题。     

What do Constructive Learners Really Learn?

In constructive induction (CI), the learner's problem representation is modified as a normal part of the learning process. This may be necessary if the initial representation is inadequate or inappropriate. However, the distinction between constructive and non-constructive methods appears to be highly ambiguous. Several conventional definitions of the process of constructive induction appear to include all conceivable learning processes. In this paper I argue that the process of constructive learning should be identified with that of relational learning (i.e., I suggest that what constructive learners really learn is relationships) and I describe some of the possible benefits that might be obtained as a result of adopting this definition.     

STRATEGIES FOR COOPERATION EMERGENCE IN DISTRIBUTED SERVICE DISCOVERY

In distributed environments where entities only have a partial view of the system, cooperation is a key issue. In the case of decentralized service discovery in open agent societies, agents only know about the services they provide and who their direct neighbors are. Therefore, they need the cooperation of their neighbors in order to locate the required services. However, cooperation is not always present in open systems. Noncooperative agents pursuing their own goals could refuse to forward queries from other agents to avoid the cost of this action; therefore, the efficiency of the decentralized service discovery could be seriously damaged. In this article, we propose the combination of incentives and local structural changes in order to promote cooperation in the service discovery process. The results show that, even in scenarios where the predominant behavior is not collaborative, cooperation emerges.     

时序模式发现算法研究

针对时序数据 ,提出一种新的时序模式的逻辑表示法 ,通过分段线性表示法 ,将时序曲线拟合为线段序列 ,从而以相对应的线段的斜率反正切值序列作为模式的逻辑表示 .在此基础上 ,设计出时序模式发现算法 ,此算法能够自动地发现所有 (子 )模式 .有关实验结果证明算法是行之有效的 .     

ROUGH KNOWLEDGE DISCOVERY FOR NUCLEAR SAFETY

Large amounts of data are generated in the investigating and maintaining of nuclear energy. Making full use of this data to gain useful insights into, for example, safety issues, presents a tremendous challenge. It is well-known that rough set theory can be applied successfully to rough classification and knowledge discovery. Our work is concerned with finding methods for using rough sets to identify classes in datasets, finding dependencies in relations and discovering rules which are hidden in databases. We believe that these methods can be used to understand and control aspects of nuclear energy generation. So in this paper we illustrate the use of our rough discovery methods by means of a running example using data on nuclear safety from the Belgian Nuclear Reactor 1 (BR1).     

基于Ad-Hoc的路由合成服务发现技术

Ad-Hoc网络是目前无线网络研究领域的热点之一.由于Ad-Hoc网络的动态拓扑、无中心节点和节点的资源有限等特点,有效的服务发现技术就尤为重要.运用了跨层设计的思想,提出将服务发现信息合成到路由中,以减少冗余的控制信息,提高服务发现效率,并通过实验模拟,验证了该方法的有效性.     

Learning hard concepts through constructive induction: framework and rationale

The intrinsic accuracy of an inductive problem is the accuracy achieved by exhaustive table look-up. Intrinsic accuracy is the upper bound for any inductive method. Hard concepts are concepts that have high intrinsic accuracy, but which cannot be learned effectively with traditional inductive methods. To learn hard concepts, we must use constructive induction - methods that create new features. We use measures of concept dispersion to explore (conceptually and empirically) the inherent weaknesses of traditional inductive approaches. These structural defects are buried in the design of the algorithms and prevent the learning of hard concepts. After studying some examples of successful and unsuccessful feature construction ("success" being defined here in terms of accuracy), we introduce a single measure of inductive difficulty that we call variation. We argue for a specific approach to constructive induction that reduces variation by incorporating various kinds of domain knowledge. All of these kinds of domain knowledge boil down to utility invariants, i.e., transformations that group together non-contiguous portions of feature space having similar class-membership values. Utility invariants manifest themselves in various ways: in some cases they exist in the user's stock of domain knowledge, in other cases they may be discovered via methods we describe.     

邻居发现协议的形式化测试

Neighbor Discovery(邻居发现 ,ND)协议是下一代互联网协议 IPv6协议中的一个重要组成部分 .随 IPv6在我国的推广应用 ,各种设备蜂拥而来 ,由于各协议实现厂家对协议的不同理解 ,通信设备的协议非一致性问题将日趋严重 ,为了检查各个不同生产厂家的实现是否与标准文本相一致 ,进而确保不同厂家的 IPv6实现之间能够互操作 ,提出了一种协议一致性测试的方法 ,并开发了 IPv6协议实现一致性测试系统 .给出了该测试系统的结构 ,并利用自定义的一种形式化描述语言 ,实现了邻居发现协议一致性测试集的形式化 .并通过对 Solaris8上的 IPv6协议实现进行测试 ,给出测试报告和结果分析 .