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.
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.
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