Kenneth D. Lawrence, New Jersey Institute of Technology, USA. Ronald Klimberg, Saint Joseph’s University, USA.

• 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.

Kenneth D. Lawrence, New Jersey Institute of Technology, USA. Ronald Klimberg, Saint Joseph’s University, USA.

• 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.