Contemporary Perspectives in Data Mining

by Kenneth D Lawrence (Editor), Ronald K Klimberg (Editor), Kenneth D Lawrence (Other), Ronald K Klimberg (Other)

Overview

A volume in Contemporary Perspectives in Data Mining Series Editors Kenneth D. Lawrence, New Jersey Institute of Technology and Ronald K. Klimberg, Saint Joseph's University The series, Contemporary Perspectives on Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. This series will be targeted both at the academic community, as well as the business practitioner. Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematical techniques. The knowledge is extracted from this data by examining the patterns of the data, whether they be associations of groups or things, predictions, sequential relationships between time order events or natural groups. Data mining applications are in marketing (customer loyalty, identifying profitable customers, instore promotions, e-commerce populations); in business (teaching data mining, efficiency of the Chinese automobile industry, moderate asset allocation funds); and techniques (veterinary predictive models, data integrity in the cloud, irregular pattern detection in a mobility network and road safety modeling.)

Product Details

ISBN-13:	9781681230870
Publisher:	Information Age Publishing, Inc.
Publication date:	07/21/2015
Series:	Contemporary Perspectives in Data Mining , #2
Pages:	238
Product dimensions:	6.14(w) x 9.21(h) x 0.50(d)

Table of Contents

Section I. Predictive Analytics
Chapter 1. Bootstrap Aggregation for Neural Network Forecasting of Supply Chain Order Quantity; Mark T. Leung and Shaotao Pan.
Chapter 2. Combining Retrospective and Predictive Analytics for More Robust Decision Support; Thomas Ott and Stephan Kudyba.
Chapter 3. Predictive Analytical Model of the CEO Compensation of Major U.S. Corporate Insurance Companies; Kenneth Lawrence, Gary Kleinman, and Sheila Lawrence.
Section II. Business Applications.
Chapter 4. Analyzing Operational and Financial Performance of U.S. Hospitals Using Two-Stage Production Process; Dinesh Pai and Hengameh Hosseini.
Chapter 5. Digital Disruption: How E-Commerce Is Changing the Grocery Game; Will Greerer, Gregory Smith, David Hyland, and Mark Frolick.
Chapter 6. The Hazards of Subgroup Analysis in Randomized Business Experiments and How to Avoid Them; B. D. McCullough.
Chapter 7. Business Intelligence Challenges for Small and Medium-Sized Business: Leveraging Existing Resources; Nick Perrino, Gregory Smith, David Hyland, and Mark Frolick.
Section III. Topics In Data Mining.
Chapter 8. Data Mining Techniques Applied to Outcome Analysis and Validation for the Futures Drug and Alcohol Rehabilitation Center; Virginia Miori and Catherine Cardamone.
Chapter 9. An Extended H-Index: A New Method to Evaluate Scientists' Impact; Feng Yang, Xiya Zu, and Zhimin Huang.
Chapter 10. Why We Need Analytics Grand Rounds; Ronald Klimberg, Richard Pollack, and Richard Herschel.
About the Editors.