二元合金溶解度间隙的集团变分法分析

应用集团变分法键近似理论,计算了原子间结合能 ε_(ij)为常数的二元合金溶解度间隙和失稳分解曲线,并就其最高临界温度 T_s 进行了理论分析。结果表明由集团变分法获得的T_s比用 Bragg-Williams 统计理论计算的临界温度低。这主要是因为集团变分法更精确地计算了溶体内能的缘故。     

虚拟计算机技术与教学演示整合的研究

网络操作系统教学中“教师演示难,学生上机难”是一个“老大难”问题.作者利用先进的虚拟计算机技术,不仅制作了能在低配置计算机上播放的,与网络群集服务教材完全配套的,能按章节点播的教学演示录像,而且还创建了一个崭新的上机实验平台,让学生能在虚拟的群集中,对两个节点的计算机和一台客户机进行完全真实的操作.成功地解决了这一困扰教师多年的难题.     

聚类分析算法在交通控制中的应用

聚类分析是根据物理或抽象对象间的相似程度对对象进行分类的一种方法，通过聚类分析使得同一类中的对象具有高的相似度，而与其他类中的对象则很不相同。PAM(Partitioning Around Medoids)算法是一种基于距离的分离式聚类方法，具有良好的抗噪声、抗偏离点的能力。本文将PAM算法应用于交通控制的时段划分中，通过验证分析．结果表明取得了良好的分类效果。     

基于映射簇的Web数据挖掘研究

传统特征选择算法在多维Web数据中由于其数据对象自身固有的稀缺性而常常失效。在典型多维Web数据挖掘应用中，不同数据对象集合对于不同雏度集合而言可能聚类会更好，且在每个簇的具体子空间中维度数将可能非常大。事实上，为所有簇查找出单个的小雏度集合是不可能的。本文应用映射簇的概念来明确簇与雏度的关系，将聚类问题转化为映射簇问题，从而简化计算提高挖掘效率。最后给出相应的算法。     

等混合法的数据处理

近年来,等混合法在矿产预报,区域构造分析,气象预报数据处理等领域中得到广泛的应用,但由于其计算工作浩繁,而只能在大型计算机上进行运算。本文使用IBM-PC微型机和应用FORTRAN77语言编制程序,实现了对某地区的构造分析,完成了等混合法的全部运算。     

Buyer similarity measures in conjoint analysis: Some alternative proposals

In the commercial application of conjoint analysis, it is not unusual to compute pairwise similarity measures for buyers based on their commonality across part-worth utilities. The resulting similarity matrix may then be processed by various clustering techniques. This paper considers other ways to measure buyer similarity in conjoint analysis and compares the resulting measures to the traditional use of part-worth commonalities. An empirical example is used to illustrate the approaches and compare their results.The authors express their appreciation to Mr. Yong-Tai Tsai, of the University of Pennsylvania, who carried out the various computer computations reported in this paper.     

HyperproⅠ：面向对象的超媒体结构研究

本文对ＨｙｐｅｒｐｒｏⅠ──一个运用面向对象方法，采用簇丛结构的超媒体信息管理系统模型进行了分析和研究．在Ｗｉｎｄｏｗｓ系统的基础上实现了面向对象方法的一般特征．通过提供结点、链和簇（模式）三种类模板建立对象结构，采用簇丛（模式）结构作为系统中表达结点组逻辑关系的一种实体，使ＨｙｐｅｒｐｒｏⅠ具有既能管理结点内容又能管理结点之间关系的功能．本文还介绍了ＨｙｐｅｒｐｒｏⅠ的结构和功能以及它的服务、查询、浏览、界面设计     

A maximum likelihood methodology for clusterwise linear regression

This paper presents a conditional mixture, maximum likelihood methodology for performing clusterwise linear regression. This new methodology simultaneously estimates separate regression functions and membership inK clusters or groups. A review of related procedures is discussed with an associated critique. The conditional mixture, maximum likelihood methodology is introduced together with the E-M algorithm utilized for parameter estimation. A Monte Carlo analysis is performed via a fractional factorial design to examine the performance of the procedure. Next, a marketing application is presented concerning the evaluations of trade show performance by senior marketing executives. Finally, other potential applications and directions for future research are identified.     

Recent convergence results for the fuzzy c-means clustering algorithms

One of the main techniques embodied in many pattern recognition systems is cluster analysis — the identification of substructure in unlabeled data sets. The fuzzy c-means algorithms (FCM) have often been used to solve certain types of clustering problems. During the last two years several new local results concerning both numerical and stochastic convergence of FCM have been found. Numerical results describe how the algorithms behave when evaluated as optimization algorithms for finding minima of the corresponding family of fuzzy c-means functionals. Stochastic properties refer to the accuracy of minima of FCM functionals as approximations to parameters of statistical populations which are sometimes assumed to be associated with the data. The purpose of this paper is to collect the main global and local, numerical and stochastic, convergence results for FCM in a brief and unified way.     

一个高可用集群的管理方案

讨论了高可用集群及其管理问题，通过采用改进的SNMP协议，并结合Web技术、数据库技术和HeartBeat方法，给出了一个方便、高效的高可用集群管理方案．