A Computational Study of Replicated Clustering with an Application to Market Segmentation*

In most commercial applications of k-means clustering, researchers choose one set of kseed points to start the partitioning process; often, the initial set of seeds is chosen randomly. Using Monte Carlo simulation, we show that significant benefits are associated with replicated starting configurations that incorporate seed selection procedures based on a hierarchical clustering of sample points drawn from the original data matrix. A real-world application of the approach is then presented.     

The Emergence of Artificial Intelligence as a Reference Discipline for Decision Support Systems Research

It is timely and appropriate to examine both philosophical and pragmatical issues associated with formalizing the adoption of artificial intelligence as a reference discipline for decision support systems research. This paper reflects on where we were when the first special issue of Decision Sciences on expert systems and decision support systems was published, addresses the dynamics of what has taken place subsequent to the publication of that first special issue, sets forth a proposition to stimulate ongoing dialog with respect to synergies between the decision support system research agenda and the research agenda of the artificial intelligence discipline, and demonstrates how the papers appearing in this follow-up special issue of Decision Sciences are representative of an emerging, challenging, and exciting new decision support systems era.     

Solving Spreadsheet-Based Integer Programming Models: An Example from International Telecommunications

Considerable effort has recently been devoted to augmenting existing spreadsheet capabilities with the power of mathematical programming. One way to accomplish this is to design mathematical programming-based solution methodologies (most likely heuristics) that exploit existing spreadsheet capabilities. We describe how this latter approach was successfully applied to an integer programming model of an international telecommunications network.     

APPLICATION OF A PRODUCTIVITY MODEL FOR COMPUTER SYSTEMS*

The performance evaluation of computer systems as they impact on final user requirements for services is a complex task. A management-oriented approach to the issue based on conventional economic theory was described in a previous report as an outgrowth of a joint university-industry research project. In this sequel we report our experience in estimating, testing, and applying this conceptual model.     

Two-dimensional projection uniformity for space-filling designs

We investigate a space-filling criterion based on $L_2$-type discrepancies, namely the uniform projection criterion, aiming at improving designs' two-dimensional projection uniformity. Under a general reproducing kernel, we establish a formula for the uniform projection criterion function, which builds a connection between rows and columns of the design. For the commonly used discrepancies, we further use this formula to represent the two-dimensional projection uniformity in terms of the $L_p$-distances of U-type designs. These results generalize existing works and reveal new links between the two seemingly unrelated criteria of projection uniformity and the maximin $L_p$-distance for U-type designs. We also apply the obtained results to study several families of space-filling designs with appealing projection uniformity. Because of good projected space-filling properties, these designs are well adapted for computer experiments, especially for the case where not all the input factors are active.     

Neural Networks: A New Tool for Predicting Thrift Failures*

A neural network model that processes input data consisting of financial ratios is developed to predict the financial health of thrift institutions. The network's ability to discriminate between healthy and failed institutions is compared to a traditional statistical model. The differences and similarities in the two modelling approaches are discussed. The neural network, which uses the same financial data, requires fewer assumptions, achieves a higher degree of prediction accuracy, and is more robust.     

Production System Development for Expert Systems Using a Recursive Partitioning Induction Approach: An Application to Mortgage,Commercial, and Consumer Lending

The concepts of expert systems and decision support systems have received considerable attention recently. While systems have been proposed for various problem areas in business, difficulties still exist in the knowledge acquisition phase of development. This paper presents a recursive partitioning analysis (RPA) approach to knowledge acquisition. The RPA production system approach was applied to data sets representing the mortgage, commercial, and consumer lending problems. Comparison of the classification rates across these problems to the results of a generalized inductive inference production system (Quinlan's ID3 algorithm) and across the mortgage and commercial lending problems to traditional statistical modeling approaches indicated that the RPA approach provided superior results while using fewer variables.     

多媒体辅助大学英语教学的有效性探讨

本文在对比分析传统教学与多媒体教学班级学生的四级统考成绩的基础上,探讨了多媒体辅助大学英语教学的有效性。多媒体辅助大学英语教学具有内容丰富、信息量大、图文并茂、生动形象等特点,其有效性充分体现在丰富性、多样性、自主性和灵活性等几方面。它倡导自主学习,为大英教学从以教师为中心的教学模式转向以学生为中心和学习为中心的教学模式提供了条件。但也有一些因素制约着多媒体辅助大学英语教学的有效性。本文主要探讨了焦虑感和距离感两大因素,并提出了解决的方法。     

Improving Computer Training Effectiveness for Decision Technologies: Behavior Modeling and Retention Enhancement*

Managers and analysts increasingly need to master the hands‐on use of computer‐based decision technologies including spreadsheet models. Effective training can prevent the lack of skill from impeding potential effectiveness gains from decision technologies. Among the wide variety of software training approaches in use today, recent research indicates that techniques based on behavior modeling, which consists of computer skill demonstration and hands‐on practice, are among the most effective for achieving positive training outcomes. The present research examines whether the established behavior‐modeling approach to software training can be improved by adding a retention enhancement intervention as a substitute for, or complement to, hands‐on practice. One hundred and eleven trainees were randomly assigned to one of three versions of a training program for spreadsheets: retention enhancement only, practice only, and retention enhancement plus practice. Results obtained while controlling for total training time indicate that a combination of retention enhancement and practice led to significantly better cognitive learning than practice alone. The initial difference in cognitive achievement was still evident one week after training. Implications for future computer training research and practice are discussed.