A NOTE ON PROCEDURES FOR TESTING THE QUALITY OF A CLUSTERING OF A SET OF OBJECTS

Despite the increased application of cluster analysis in decision sciences, few attempts have been made to derive hypothesis-testing procedures for the evaluation of clustering solutions. In fact, the present paper shows that at least one such attempt failed to specify a meaningful sampling distribution for the test procedure. An alternative index based on the concept of point-biserial correlation is proposed as a possible recovery measure. The index is subsequently used to form the basis of a valid statistical test for the existence of cluster structure.     

激光照排系统运行技术初探

根据多年从事激光照排系统维护工作的经验,对该系统运行中出现的技术问题进行了分析探讨,并给出了处理问题的具体办法和措施。     

《黑暗来临前的夏天》的叙事策略探究

陶丽丝·莱辛(DorisLessing,1919-)是战后英国最有独创精神的一名杰出女作家。她的作品常以罕见的深度和广度反映了妇女的精神世界和生存境况。《黑暗来临前的夏天》(TheSummerbeforetheDark)是莱辛所写的最为畅销的一部小说,作品描写了中年妇女凯特在一个夏天抛弃了女性的传统模式进行艰难的自我定义和自我寻求的历程。本文主要从叙事视角、叙事人物、叙事手法等方面对该作品的叙事策略进行探讨。     

“陕西导游”精品课程的建设与实践

“陕西导游”课程是陕西省省级精品课程,也是高职旅游管理专业的核心课程之一,本文从高等职业教育教学质量和教学改革的理论高度,结合长期的教学实践,提出了以掌握概念、突出实践、强化应用、培养技能为重点,建设要以优化教学内容为核心,以现代信息技术手段为平台,以推进教学资源共享为原则,以项目化训练为实践教学的载体等观点,对贯彻教育部关于全面提高高等职业教育教学质量具有理论意义和实践指导作用。     

Theoretical Distributions of Optima for Univariate Discrimination of Random Data*

Optimal linear discriminant models maximize percentage accuracy for dichotomous classifications, but are rarely used because a theoretical framework that allows one to make valid statements about the statistical significance of the outcomes of such analyses does not exist. This paper describes an analytic solution for the theoretical distribution of optimal values for univariate optimal linear discriminant analysis, under the assumption that the data are random and continuous. We also present the theoretical distribution for sample sizes up to N= 30. The discovery of a statistical framework for evaluating the performance of optimal discriminant models should greatly increase their use by scientists in all disciplines.     

Marketplace Competition in the Personal Computer Industry*

A decision regarding development and introduction of a potential new product depends, in part, on the intensity of compeitition anticipated in the marketplace. In the case of a technology-based product such as a personal computer (PC), the number of competing products may be very dynamic and consequently uncertain. We address this problem by modeling growth in the number of new PCs as a stochastic counting process, incorporating product entries and exits. We demonstrate how to use the resulting model to forecast competition five years in advance.     

Introducing Discriminant Analysis to the Business Statistics Curriculum*

Recently a good deal of interest and effort has been directed toward making statistics courses more effective in business schools. It is believed that a key to success in this area involves giving a more prominent role to statistical tools which are useful in actual business practice. If the research literature is any indication, discriminant analysis (DA) has many potential applications in virtually all areas of business. Yet, DA is rarely taught in undergraduate business and/or M.B.A. statistics courses. This is partially due to the fact that most presentations of DA are relegated to multivariate statistics texts that assume an advanced knowledge of linear algebra. This paper attempts to rectify this situation by proposing a simplified pedagogical approach for introducing linear DA in undergraduate and/or M.B.A. business statistics courses.     

An Efficient Optimal Solution Algorithm for the Classification Problem

A new LP-based algorithm is developed to determine the optimal solution to the two-group classification problem. The procedure is efficient when compared to current research in a simulation study.     

Separation Failure in Linear Programming Discriminant Models

Linear programming discriminant analysis (LPDA) models are designed around a variety of objective functions, each representing a different measure of separation of the training samples by the resulting discriminant function. A separation failure is defined to be the selection of an “optimal” discriminant function which incompletely separates a pair of completely separable training samples. Occurrence of a separation failure suggests that the chosen discriminant function may have an unnecessarily low classification accuracy on the actual populations involved. In this paper, a number of the LPDA models proposed for the two-group case are examined to learn which are subject to separation failure. It appears that separation failure in any model can be avoided by applying the model twice, reversing group designations.     

Improved Linear Programming Models for Discriminant Analysis*

Discriminant analysis is an important tool for practical problem solving. Classical statistical applications have been joined recently by applications in the fields of management science and artificial intelligence. In a departure from the methodology of statistics, a series of proposals have appeared for capturing the goals of discriminant analysis in a collection of linear programming formulations. The evolution of these formulations has brought advances that have removed a number of initial shortcomings and deepened our understanding of how these models differ in essential ways from other familiar classes of LP formulations. We will demonstrate, however, that the full power of the LP discriminant analysis models has not been achieved, due to a previously undetected distortion that inhibits the quality of solutions generated. The purpose of this paper is to show how to eliminate this distortion and thereby increase the scope and flexibility of these models. We additionally show how these outcomes open the door to special model manipulations and simplifications, including the use of a successive goal method for establishing a series of conditional objectives to achieve improved discrimination.