Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to identify previously unknown patterns.
Knowledge Discovery Process and Methods to Enhance Organizational Performance explains the knowledge discovery and data mining (KDDM) process in a manner that makes it easy for readers to implement. Sharing the insights of international KDDM experts, it details powerful strategies, models, and techniques for managing the full cycle of knowledge discovery projects. The book supplies a process-centric view of how to implement successful data mining projects through the use of the KDDM process. It discusses the implications of data mining including security, privacy, ethical and legal considerations.
- Provides an introduction to KDDM, including the various models adopted in academia and industry
- Details critical success factors for KDDM projects as well as the impact of poor quality data or inaccessibility to data on KDDM projects
- Proposes the use of hybrid approaches that couple data mining with other analytic techniques (e.g., data envelopment analysis, cluster analysis, and neural networks) to derive greater value and utility
- Demonstrates the applicability of the KDDM process beyond analytics
- Shares experiences of implementing and applying various stages of the KDDM process in organizations
The book includes case study examples of KDDM applications in business and government. After reading this book, you will understand the critical success factors required to develop robust data mining objectives that are in alignment with your organization’s strategic business objectives.
Table of Contents
Introduction; Kweku-Muata Osei-Bryson and Corlane Barclay
Overview on Knowledge Discovery via Data Mining Process Models; Sumana Sharma
An Integrated Knowledge Discovery and Data Mining Process Model; Sumana Sharma and Kweku-Muata Osei-Bryson
A Method for Formulating the Business Objectives of Data Mining Projects; Sumana Sharma and Kweku-Muata Osei-Bryson
The Application of the Business Understanding Phase of the CRISP-DM Approach to a Knowledge Discovery Project on Education; Corlane Barclay
A Context-Aware Framework for Supporting the Evaluation of Data Mining Results; Kweku-Muata Osei-Bryson
Issues and Considerations in the Application of Data Mining in Business; Edward Chen
The Importance of Data Quality Assurance to the Data Analysis Activities of the Data Mining Process; Patricia E. Nalwoga Lutu
Critical Success Factors in Knowledge Discovery and Data Mining Projects; Corlane Barclay
Data Mining in Organizations: Opportunities and Challenges for Small Developing States; Corlane Barclay
Determining Sources of Relative Inefficiency in Heterogeneous Samples Using Multiple Data Analytic Techniques; Sergey Samoilenko and Kweku-Muata Osei-Bryson
Applications of Data Mining in Organizational Behavior; Arash Shahin and Reza Salehzadeh
Decision Making and Decision Styles of Project Managers: A Preliminary Exploration Using Data Mining Techniques; Kweku-Muata Osei-Bryson and Corlane Barclay
The Application of the CRISP-DM Process in Predicting High School Students’ Examination (CSEC/CXC) Performance; Corlane Barclay, Andrew Dennis, and Jerome Shepherd
Post-Pruning in Decision Tree Induction Using Multiple Performance Measures; Kweku-Muata Osei-Bryson
Selecting Classifiers for an Ensemble—An Integrated Ensemble Generation Procedure; Kweku-Muata Osei-Bryson
A New Feature Selection Technique Applied to Credit Scoring Data Using a Rank Aggregation Approach Based on Optimization, Genetic Algorithm, and Similarity; Bouaguel Waad, Bel Mufti Ghazi, and Limam Mohamed
Kweku-Muata Osei-Bryson is a professor of information systems (IS) at Virginia Commonwealth University in Richmond, Virginia, where he also served as the coordinator of the IS PhD program during 2001–2003. He is also a visiting professor of computing at the University of the West Indies at Mona, Kingston, Jamaica. Previously, he was a professor of information systems and decision sciences at Howard University in Washington, D.C., United States. He has also worked as an IS practitioner in the industry and government. He holds a doctorate degree in applied mathematics (management science and information systems) from the University of Maryland at College Park; an MS degree in systems engineering from Howard University, and a bachelor’s degree in natural sciences from the University of the West Indies at Mona, Kingston, Jamaica.
His research areas include data mining, decision support systems, knowledge management, IS security, e-Commerce, information technology for development, database management, IS outsourcing, and multicriteria decision making.
Corlane Barclay is a business consultant and a full-time lecturer at the University of Technology, Jamaica, since 2009, where she has designed and successfully implemented the first and only wholly owned graduate program in information systems management, with five specializations, of the School of Computing and Information Technology in 2011. She also served as a coordinator for this program between 2011 and 2012. She is a certified project manager, with a PMP® certification, with over 10 years of industry and government experience. She also holds a doctorate degree in information systems and an MS degree in information systems and bachelor’s degree in management and accounting and law from the University of the West Indies, Mona campus. She is currently in the final year at the Norman Manley Law School, Mona, Kingston, Jamaica, completing the certificate of legal education, which prepares for admission to practice in the Commonwealth Caribbean territories.
Her research interests include cyber security and cybercrime, project performance and project success, technology and telecommunications law, information and communication technologies for development, and knowledge discovery and data mining models.