Contemporary Perspectives in Data Mining
Volume 1
Published on 7. December 2012
Book
Hardback
254 pages
978-1-62396-056-8 (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 form 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 seen in finance (banking, brokerage, insurance), marketing (customer relationships, retailing, logistics, travel), as well as in manufacturing, health care, fraud detection, home-land 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 form 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 seen in finance (banking, brokerage, insurance), marketing (customer relationships, retailing, logistics, travel), as well as in manufacturing, health care, fraud detection, home-land security, and law enforcement.
More details
Series
Language
English
Place of publication
Charlotte
United States
Publishing group
Emerald Publishing Inc
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 18 mm
Weight
549 gr
ISBN-13
978-1-62396-056-8 (9781623960568)
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 A: Methodological Studies.
Chapter 1. Frame Selection Based on Mixtures of Trees in Discrete Data, Hui Zhao, Xing Wang, Wei Yuan, Susan X. Li, and Zhimin Huang.
Chapter 2. Data Mining Techniques for Quality Improvement, Seoung Bum Kim.
Chapter 3. Big Bang Data Generation: Reinforcement for the Discriminant Problem, Gregory Smith.
Chapter 4. Business Analytics: Today's Green? Ronald K. Klimberg and B. D. McCullough.
Chapter 5. Change Point Plots: A Graphical Method for Identifying Changes in the Distribution of a Random Variable Over Time, James J. Cochran.
Section B: Financial Studies.
Chapter 6. Discovering the Co-movement Structure of Chinese Stock Market by SPACE with EM Algorithm, ShiYuan He, Xing Wang, Wei Yuan, Susan X. Li, and Zhimin Huang.
Chapter 7. Knowledge Discovery for Continuous Financial Assurance Using Multiple Types of Digital Information, Daniel E. O'Leary.
Chapter 8. Regression Estimation of a Cost Function with Severe Data Problems and Extreme Values of Observations in the Maintenance and Repair Activities of Backbone Internet Providers, Kenneth D. Lawrence, Dinesh R. Pai, and Sheila M. Lawrence.
Section C: Behavioral Studies.
Chapter 9. Data Mining's Usefulness for Assessing Market Segmentation Performance, Paul Mangiameli, Illya Mowerman, Albert Della Bitta, and James Mangiameli.
Chapter 10. Measuring the Semantic and Representational Consistency of Interconnected Structured and Unstructured Data for Data Mining Applications, Roger Blake and Paul Mangiameli.
Chapter 11. Clustering and Principal Components Analyses to Understand Student Motivations and Ethical Approaches to Academic Ethics with Recommendations for Curricular Change, Virginia M. Miori, Kelly A. Doyle, and Kathleen Campbell.
About the Editor.
Chapter 1. Frame Selection Based on Mixtures of Trees in Discrete Data, Hui Zhao, Xing Wang, Wei Yuan, Susan X. Li, and Zhimin Huang.
Chapter 2. Data Mining Techniques for Quality Improvement, Seoung Bum Kim.
Chapter 3. Big Bang Data Generation: Reinforcement for the Discriminant Problem, Gregory Smith.
Chapter 4. Business Analytics: Today's Green? Ronald K. Klimberg and B. D. McCullough.
Chapter 5. Change Point Plots: A Graphical Method for Identifying Changes in the Distribution of a Random Variable Over Time, James J. Cochran.
Section B: Financial Studies.
Chapter 6. Discovering the Co-movement Structure of Chinese Stock Market by SPACE with EM Algorithm, ShiYuan He, Xing Wang, Wei Yuan, Susan X. Li, and Zhimin Huang.
Chapter 7. Knowledge Discovery for Continuous Financial Assurance Using Multiple Types of Digital Information, Daniel E. O'Leary.
Chapter 8. Regression Estimation of a Cost Function with Severe Data Problems and Extreme Values of Observations in the Maintenance and Repair Activities of Backbone Internet Providers, Kenneth D. Lawrence, Dinesh R. Pai, and Sheila M. Lawrence.
Section C: Behavioral Studies.
Chapter 9. Data Mining's Usefulness for Assessing Market Segmentation Performance, Paul Mangiameli, Illya Mowerman, Albert Della Bitta, and James Mangiameli.
Chapter 10. Measuring the Semantic and Representational Consistency of Interconnected Structured and Unstructured Data for Data Mining Applications, Roger Blake and Paul Mangiameli.
Chapter 11. Clustering and Principal Components Analyses to Understand Student Motivations and Ethical Approaches to Academic Ethics with Recommendations for Curricular Change, Virginia M. Miori, Kelly A. Doyle, and Kathleen Campbell.
About the Editor.