Knowledge Discovery Process and Methods to Enhance Organizational Performance

Kweku-Muata Osei-Bryson, Corlane Barclay

• Publisher: Auerbach Publications
• Year: 2015
• ISBN:
  9781482212389
• Edition: 1
• Topic: Economia
• Subtopic: Teoria economica

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

• Front Cover
• Contents
• Preface
• Editors
• Contributors
• Chapter 1: Introduction
• Chapter 2: Overview of Knowledge Discovery and Data Mining Process Models
• Chapter 3: An Integrated Knowledge Discovery and Data Mining Process Model
• Chapter 4: A Novel Method for Formulating the Business Objectives of Data Mining Projects
• Chapter 5: The Application of the Business Understanding Phase of the CRISP-DM Approach to a Knowledge Discovery Project on Education
• Chapter 6: A Context-Aware Framework for Supporting the Evaluation of Data Mining Results
• Chapter 7: Issues and Considerations in the Application of Data Mining in Business
• Chapter 8: The Importance of Data Quality Assurance to the Data Analysis Activities of the Data Mining Process
• Chapter 9: Critical Success Factors in Knowledge Discovery and Data Mining Projects
• Chapter 10: Data Mining for Organizations: Challenges and Opportunities for Small Developing States
• Chapter 11: Determining Sources of Relative Inefficiency in Heterogeneous Samples Using Multiple Data Analytic Techniques
• Chapter 12: Applications of Data Mining in Organizational Behavior
• Chapter 13: Decision Making and Decision Styles of Project Managers: A Preliminary Exploration Using Data Mining Techniques
• Chapter 14: Application of the CRISP-DM Model in Predicting High School Students’ Examination (CSEC/CXC) Performance
• Chapter 15: Post-Pruning in Decision Tree Induction Using Multiple Performance Measures
• Chapter 16: Selecting Classifiers for an Ensemble—An Integrated Ensemble Generation Procedure
• Chapter 17: A New Feature Selection Technique Applied to Credit Scoring Data Using a Rank Aggregation Approach Based on Optimization, Genetic Algorithm, and Similarity
• Back Cover