Contemporary Perspectives in Data Mining
Published on 21. July 2015
Book
Hardback
238 pages
978-1-68123-088-7 (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 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.)
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.)
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: 17 mm
Weight
526 gr
ISBN-13
978-1-68123-088-7 (9781681230887)
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, USA
Ronald Klimberg, Saint Joseph's University, USA
Ronald Klimberg, Saint Joseph's University, USA
Content
Section I: Marketing Applications.
Chapter 1. Data Privacy in Loyalty Programs: An Exploratory Investigation, David Burns and Gregory Smith.
Chapter 2. Identifying Profitable Customers Using a Two-Stage Logistic Model: An Application from B2B Credit Card Marketing, Vernon Gerety and Stephan Kudyba.
Chapter 3. A Fractional Factorial Analysis for In-Store Promotions, Peter Charette, John Stanton, and Neal Hooker.
Chapter 4. Methods for Customer Analytics of Heterogeneous E-Commerce Populations, Ruben Mancha and Mark T. Leung.
Section II: Business Applications.
Chapter 5. Teaching a Data Mining Course in a Business School, Ronald K. Klimberg.
Chapter 6. Measuring the Market Efficiency of Chinese Automobile Industry by Using a Max-Min DEA Model, Feng Yang, Hangting Hu, Chenchen Yang, and Zhimin Huang.
Chapter 7. A Clustering Analysis of Five-Star Morning Star Ruled Moderate Asset Allocation Funds, Kenneth D. Lawrence, Gary Kleinman, and Sheila M. Lawrence.
Section III: Techniques.
Chapter 8. Data Mining Techniques Applied to the Study of Canines with Osteoarthritis: Developing a Predictive Model, Virginia M. Miori.
Chapter 9. Data Mining Techniques for Information Assurance and Data Integrity on the Cloud, Alla Kammerdiner.
Chapter 10. Multivariate Copulas Model in Spatiotemporal Irregular Pattern Detection in Mobility Network, Rong Duan and Guang-Qin Ma.
Chapter 11. Road Safety Detection Modeling Based on Vehicle Monitoring Data in China, Xing Wang, Wei Yuan, Susan X. Li, and Zhimin Huang.
About the Editors.
Chapter 1. Data Privacy in Loyalty Programs: An Exploratory Investigation, David Burns and Gregory Smith.
Chapter 2. Identifying Profitable Customers Using a Two-Stage Logistic Model: An Application from B2B Credit Card Marketing, Vernon Gerety and Stephan Kudyba.
Chapter 3. A Fractional Factorial Analysis for In-Store Promotions, Peter Charette, John Stanton, and Neal Hooker.
Chapter 4. Methods for Customer Analytics of Heterogeneous E-Commerce Populations, Ruben Mancha and Mark T. Leung.
Section II: Business Applications.
Chapter 5. Teaching a Data Mining Course in a Business School, Ronald K. Klimberg.
Chapter 6. Measuring the Market Efficiency of Chinese Automobile Industry by Using a Max-Min DEA Model, Feng Yang, Hangting Hu, Chenchen Yang, and Zhimin Huang.
Chapter 7. A Clustering Analysis of Five-Star Morning Star Ruled Moderate Asset Allocation Funds, Kenneth D. Lawrence, Gary Kleinman, and Sheila M. Lawrence.
Section III: Techniques.
Chapter 8. Data Mining Techniques Applied to the Study of Canines with Osteoarthritis: Developing a Predictive Model, Virginia M. Miori.
Chapter 9. Data Mining Techniques for Information Assurance and Data Integrity on the Cloud, Alla Kammerdiner.
Chapter 10. Multivariate Copulas Model in Spatiotemporal Irregular Pattern Detection in Mobility Network, Rong Duan and Guang-Qin Ma.
Chapter 11. Road Safety Detection Modeling Based on Vehicle Monitoring Data in China, Xing Wang, Wei Yuan, Susan X. Li, and Zhimin Huang.
About the Editors.