Contemporary Perspectives in Data Mining
Published on 15. October 2020
Book
Hardback
164 pages
978-1-64802-144-2 (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 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.
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 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.
More details
Series
Language
English
Place of publication
United States
Publishing group
Emerald Publishing Inc
Target group
Professional and scholarly
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 14 mm
Weight
417 gr
ISBN-13
978-1-64802-144-2 (9781648021442)
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
Other editions
Additional editions
E-Book
01/2021
1st Edition
Information Age Publishing
from
€62.33
Available for download
Persons
Content
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.
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.