Contemporary Perspectives in Data Mining
The series, Contemporary Perspectives in Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. The series is 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 there are associations of groups or things, predictions, sequential relationships between time order events or natural groups. Data mining applications are in business (banking, brokerage, and insurance), marketing (customer relationship, retailing, logistics, and travel), as well as in manufacturing, health care, fraud detection, homeland security and law enforcement.

1113871199
Contemporary Perspectives in Data Mining
The series, Contemporary Perspectives in Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. The series is 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 there are associations of groups or things, predictions, sequential relationships between time order events or natural groups. Data mining applications are in business (banking, brokerage, and insurance), marketing (customer relationship, retailing, logistics, and travel), as well as in manufacturing, health care, fraud detection, homeland security and law enforcement.

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Contemporary Perspectives in Data Mining

Contemporary Perspectives in Data Mining

Contemporary Perspectives in Data Mining

Contemporary Perspectives in Data Mining

Overview

The series, Contemporary Perspectives in Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. The series is 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 there are associations of groups or things, predictions, sequential relationships between time order events or natural groups. Data mining applications are in business (banking, brokerage, and insurance), marketing (customer relationship, retailing, logistics, and travel), as well as in manufacturing, health care, fraud detection, homeland security and law enforcement.

Product Details

ISBN-13: 9798887308531
Publisher: Emerald Publishing Limited
Publication date: 05/19/2025
Series: Contemporary Perspectives in Data Mining , #5
Pages: 186
Product dimensions: 6.14(w) x 9.21(h) x 0.40(d)

About the Author

Kenneth D. Lawrence is a Professor of Management Science and Business Analytics at the Tuchman School of Management at the New Jersey Institute of Technology, USA.

Ronald K. Klimberg, PhD, is a Professor in the Decision and System Sciences Department of the Haub School of Business at Saint Joseph’s University, USA.

Table of Contents

Section I. Forecasting and Data Mining.
Chapter 1. Combining Forecasting Methods: Predicting Quarterly Sales in 2019 for Motorola Solutions; Kenneth D. Lawrence, Stephan Kudyba, and Sheila M. Lawrence.
Chapter 2. Bayesian Deep Generative Machine Learning for Real Exchange Rate Forecasting; Mark T. Leung, Shaotao Pan, and An-Sing Chen.
Chapter 3. Predicting Hospital Admissions and Surgery Based on Fracture Severity: An Exploratory Study; Aishwarya Mohanakrishnan, Dinesh R. Pai, and Girish H. Subramanian.
Section II. Business Intelligence And Optimization.
Chapter 4. Business Intelligence and the Millennials: Data Driven Strategies for America's Largest Generation; Joel Thomas Asay, Gregory Smith, and Jamie Pawlieukwicz.
Chapter 5. Data Driven Portfolio Optimization With Drawndown Constraints Using Machine Learning; Meng-Chen Hsieh.
Chapter 6. Mining for Fitness: Analytical Models That Fit You So You Can Be Fit; William Asterino and Kathleen Campbell.
Section III. Business Applications Of Data Mining.
Chapter 7. H Index Weighted by Eigenfactors of Citations for Journal Evaluation; Cuihua Hu, Feng Yang, Xiya Zu, and Zhimin Huang.
Chapter 8. A Method to Determine the Size of the Resampled Data in Imbalanced Classification; Matthew Bonas, Son Nguyen, Alan Olinsky, John Quinn, and Phyllis Schumacher.
Chapter 9. Performance Measure Analysis of the American Water Work Company by Statistical Clustering; Kenneth D. Lawrence, Stephen K. Kudbya, and Sheila M. Lawrence.
About the Authors.

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