Contemporary Perspectives in Data Mining
Volume 3
1st Edition
Published on 9. January 2017
169 pages
978-1-64113-056-1 (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 practitione.
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 group.
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 group.
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.
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Kenneth D. Lawrence | Ronald K. Klimberg
Contemporary Perspectives in Data Mining
Book
09/2017
Information Age Publishing
€62.30
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Content
1 - Cover [Seite 1]
2 - Series page [Seite 2]
3 - Contemporary Perspectives in Data Mining: Volume 3 [Seite 4]
4 - Library of Congress Cataloging-in-Publication Data [Seite 5]
5 - Contents [Seite 6]
6 - SECTION I: PREDICTIVE ANALYTICS [Seite 8]
6.1 - CHAPTER 1: Bootstrap Aggregation for Neural Network Forecasting of Supply Chain Order Quantity [Seite 10]
6.2 - CHAPTER 2: Combining Retrospective and Predictive Analytics for More Robust Decision Support [Seite 36]
6.3 - CHAPTER 3: Predictive Analytical Model of the CEO Compensation of Major U.S. Corporate Insurance Companies [Seite 48]
7 - SECTION II: BUSINESS APPLICATIONS [Seite 54]
7.1 - CHAPTER 4: Analyzing Operational and Financial Performance of U.S. Hospitals Using Two-Stage Production Process [Seite 56]
7.2 - CHAPTER 5: Digital Disruption [Seite 74]
7.3 - CHAPTER 6: The Hazards of Subgroup Analysis in Randomized Business Experiments and How to Avoid Them [Seite 86]
7.4 - CHAPTER 7: Business Intelligence Challenges for Small and Medium-Sized Business [Seite 100]
8 - SECTION III: TOPICS IN DATA MINING [Seite 110]
8.1 - CHAPTER 8: Data Mining Techniques Applied to Outcome Analysis and Validation for the Futures Drug and Alcohol Rehabilitation Center [Seite 112]
8.2 - CHAPTER 9: An Extended H-Index [Seite 142]
8.3 - CHAPTER 10: Why We Need Analytics Grand Rounds [Seite 154]
9 - ABOUT THE EDITORS [Seite 168]
2 - Series page [Seite 2]
3 - Contemporary Perspectives in Data Mining: Volume 3 [Seite 4]
4 - Library of Congress Cataloging-in-Publication Data [Seite 5]
5 - Contents [Seite 6]
6 - SECTION I: PREDICTIVE ANALYTICS [Seite 8]
6.1 - CHAPTER 1: Bootstrap Aggregation for Neural Network Forecasting of Supply Chain Order Quantity [Seite 10]
6.2 - CHAPTER 2: Combining Retrospective and Predictive Analytics for More Robust Decision Support [Seite 36]
6.3 - CHAPTER 3: Predictive Analytical Model of the CEO Compensation of Major U.S. Corporate Insurance Companies [Seite 48]
7 - SECTION II: BUSINESS APPLICATIONS [Seite 54]
7.1 - CHAPTER 4: Analyzing Operational and Financial Performance of U.S. Hospitals Using Two-Stage Production Process [Seite 56]
7.2 - CHAPTER 5: Digital Disruption [Seite 74]
7.3 - CHAPTER 6: The Hazards of Subgroup Analysis in Randomized Business Experiments and How to Avoid Them [Seite 86]
7.4 - CHAPTER 7: Business Intelligence Challenges for Small and Medium-Sized Business [Seite 100]
8 - SECTION III: TOPICS IN DATA MINING [Seite 110]
8.1 - CHAPTER 8: Data Mining Techniques Applied to Outcome Analysis and Validation for the Futures Drug and Alcohol Rehabilitation Center [Seite 112]
8.2 - CHAPTER 9: An Extended H-Index [Seite 142]
8.3 - CHAPTER 10: Why We Need Analytics Grand Rounds [Seite 154]
9 - ABOUT THE EDITORS [Seite 168]
