Contemporary Perspectives in Data Mining
Published on 26. September 2017
Book
Hardback
170 pages
978-1-64113-055-4 (ISBN)
Description
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 finance (banking, brokerage, and insurance), marketing (customer relationships, retailing, logistics, and travel), as well as in manufacturing, health care, fraud detection, homeland security, and law enforcement.
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 finance (banking, brokerage, and insurance), marketing (customer relationships, retailing, logistics, and travel), as well as in manufacturing, health care, fraud detection, homeland security, and law enforcement.
More details
Series
Language
English
Place of publication
Charlotte
United States
Publishing group
Emerald Publishing Inc
Target group
Professional and scholarly
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 14 mm
Weight
426 gr
ISBN-13
978-1-64113-055-4 (9781641130554)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Kenneth D. Lawrence, New Jersey Institute of Technology.
Ronald K. Klimberg, Saint Joseph's University.
Ronald K. Klimberg, Saint Joseph's University.
Content
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.
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.