GDQR法用于输流曲管的流致振动研究

将广义微分求积法(GDQR)用于分析输流曲管的流致振动问题,这是一个新的尝试．基于输流曲管的面内振动微分方程,利用GDQR法使曲管系统在空间域上得以离散化,从而获得了输流曲管的动力学方程组．数值算例中,计算得到了输流曲管在几种典型边界条件下的固有频率以及曲管发生失稳的临界流速等,这些计算结果与前人的解析解结果吻合较好．此外,还给出了两端固定输流曲管典型的动力响应行为．研究表明,GDQR法极易处理输流曲管这一类动力学模型,精度令人满意,进一步的研究可望推广到输流管道的非线性振动分析中．     

基于粗糙集理论的关联规则挖掘模型

提出了一个基于粗糙集理论的关联规则挖掘模型。介绍了该规则挖掘模型的主要步骤，模型中应用了属性约简和规则约简技术，并给出了该两个技术的算法。     

基于数据挖掘技术的虚拟社区成员行为研究

虚拟社区的研究对于社区的建设、管理和发展都具有重要意义。然而目前对虚拟社区和社区中的成员行为的定量研究还不多见，本文抽样采集了某一典型虚拟社区中的成员文章数据，在进行预处理中，利用关联规则的数据挖掘技术对其进行了挖掘，从而获取了虚拟社区中一些潜在的行为规律，并给出这些行为规律的相应描述，其研究结果具有一定的意义，对虚拟社区的建设、发展和管理起到预见和指导作用。     

一种关联规则挖掘的优化算法

文中基于chi-square检验、有趣度及信息增益理论,给出了一种挖掘优化关联规则的算法。该算法将冗余的规则分为两个部分:一部分规则缺乏统计的相关性,而另外一部分规则不满足“新奇的”要求。实验结果表明算法可以有效地去除冗余规则并提高挖掘效率。     

Multi-Path Reasoning in a Database

Rules have been used in a database context for several purposes:deductive database queries, active database triggers, and productionsystem programs. Exploring the search space for non-deterministicrule programs, however, has generally been available only in largemonolithic systems intended for artificial intelligence applications.The goal of this research is to provide multi-path reasoning fornon-deterministic rule programs in a database environment. Themotivation for this work includes the increasing use ofdatabase-style triggers to assist data-intensive applications, e.g.,the human genome project and the Intelligent Integration ofInformation (I ³) Program.A non-deterministic rule program is one where there is more than oneterminal state. Such programs are generally not consideredappropriate for database queries where the focus is on rules programsor techniques that guarantee a unique fixed point. Butnon-deterministic programs are commonly used for various heuristicsearch problems such as the traveling salesperson problem. With anon-deterministic program, it is particularly important to beflexible about the order in which the search space is exploredbecause exhaustive search is generally not feasible. Thecontributions of this research are: the representation of themulti-path search tree within a database and the ability for theproblem solver to control the search process for a non-deterministicrule program.     

Entropy maximisation and open queueing networks with priorities and blocking

A review is carried out on the characterisation and algorithmic implementation of an extended product-form approximation, based on the principle of maximum entropy (ME), for a wide class of arbitrary finite capacity open queueing network models (QNMs) with service and space priorities. A single server finite capacity GE/GE/1/N queue with R (R>1) distinct priority classes, compound Poisson arrival processes (CPPs) with geometrically distributed batches and generalised exponential (GE) service times is analysed via entropy maximisation, subject to suitable GE-type queueing theoretic constraints, under preemptive resume (PR) and head-of-line (HOL) scheduling rules combined with complete buffer sharing (CBS) and partial buffer sharing (PBS) management schemes stipulating a sequence of buffer thresholds {N=(N₁,…,N_R),0<N_i≤N_i−1,i=2,…,R}. The GE/GE/1/N queue is utilised, in conjunction with GE-type first two moment flow approximation formulae, as a cost-effective building block towards the establishment of a generic ME queue-by-queue decomposition algorithm for arbitrary open QNMs with space and service priorities under repetitive service blocking with random destination (RS-RD). Typical numerical results are included to illustrate the credibility of the ME algorithm against simulation for various network topologies and define experimentally pessimistic GE-type performance bounds. Remarks on the extensions of the ME algorithm to other types of blocking mechanisms, such as repetitive service blocking with fixed destination (RS-FD) and blocking-after-service (BAS), are included.     

DualMiner: A Dual-Pruning Algorithm for Itemsets with Constraints

Recently, constraint-based mining of itemsets for questions like find all frequent itemsets whose total price is at least $50 has attracted much attention. Two classes of constraints, monotone and antimonotone, have been very useful in this area. There exist algorithms that efficiently take advantage of either one of these two classes, but no previous algorithms can efficiently handle both types of constraints simultaneously. In this paper, we present DualMiner, the first algorithm that efficiently prunes its search space using both monotone and antimonotone constraints. We complement a theoretical analysis and proof of correctness of DualMiner with an experimental study that shows the efficacy of DualMiner compared to previous work.     

Maintaining discovered frequent itemsets: Cases for changeable database and support

Mining frequent itemsets from large databases has played an essential role in many data mining tasks. It is also important to maintain the discovered frequent itemsets for these data mining tasks when the database is updated. All algorithms proposed so far for the maintenance of discovered frequent itemsets are only performed with a fixed minimum support,which is the same as that used to obtain the discovered frequent itemsets. That is, users cannot change the minimum support even if the new results are unsatisfactory to the users. In this paper two new complementary algorithms, FMP (First Maintaining Process) and RMP (Repeated Maintaining Process), are proposed to maintain discovered frequent itemsets in the case that new transaction data are added to a transaction database. Both algorithms allow users to change the minimum support for the maintenance processes. FMP is used for the first maintaining process, and when the result derived from the FMP is unsatisfactory, RMP will be performed repeatedly until satisfactory results are obtained. The proposed algorithms re-use the previous results to cut down the cost of maintenance. Extensive experiments have been conducted to assess the performance of the algorithms. The experimental results show that the proposed algorithms are very resultful compared with the previous mining and maintenance algorithms for maintenance of discovered frequent itemsets.     

一种基于粗集理论的动态近似规则挖掘推理方法

提出一种基于粗集理论的,把属性的重要性和属性值的出现频率综合起来进行规则推理的方法.分析了“激活一个,否则离开”原则的优缺点,指出在近似推理中,大前提中的规则数量应该可变.给出一种根据推理过程中规则的出现频率决定其是否保留,从而实现规则数量的动态变化的方法,证明了动态变化过程中规则的数量不会无限增加.实例表明此法是比较有效的.