A Structured Approach to Data Mining offers a systematic introduction to data mining for students, researchers, and practitioners in computing and information systems. It emphasises conceptual understanding, methodological reasoning, and appropriate application of techniques rather than software-specific implementation.
Table of Contents:
List of Figures
List of Tables
Foreword
Preface
Introduction
Chapter 1: Introduction -The Roots of Data Mining -Setting the Context: The Boundaries of Data Mining -Distinguishing Artificial Intelligence, Data Mining, Machine Learning, and Deep Learning -Data Mining Process and Its Core Phases -The Concept of "Model" in Data Mining -Structure of This Book
Chapter 2: Problem Understanding -Overview -Stakeholder Involvement and Domain Knowledge Integration -Objective and Problem Formulation -Critical Questions in Problem Understanding -The ProFUMe Methodology -Conclusion
Chapter 3: Data Understanding -Overview -How Data Understanding Impacts Subsequent -Data Mining Phases -Data Understanding Through EDA -Data Structure -Data Distribution -Data Quality -Conclusion
Chapter 4: Data Preprocessing -Overview -Importance of Data Preprocessing -Core Tasks of Data Preprocessing -Methods for Handling Missing Values -Methods for Addressing Outliers -Methods for Handling Inconsistent Data -Methods for Solving Irrelevant Data -Methods for Addressing Duplicate Data -Methods for Addressing Imbalanced Data -Encoding Methods for Addressing Machine Learning -Algorithm Suitability -Conclusion
Chapter 5: Data Modelling (Introduction) -Overview -Supervised Learning Approach -Unsupervised Learning Approach -Unsupervised Learning Tasks: Clustering and Association Rule Mining -Semi-Supervised Learning -Comparison of Supervised, Unsupervised, and Semi-Supervised Approaches -Machine Learning Approaches, Techniques, and Algorithms -Periodicity of Data Modelling -Conclusion
Chapter 6: Data Modelling (Decision Trees) -Overview -Types of Decision Trees -Fundamental Concepts of Decision Trees -Decision Tree Construction Algorithms -Decision Tree Algorithms and Their Considerations -Strengths and Limitations of Decision Trees -Conclusion
Chapter 7: Data Modelling (Regressions) -Overview -Fundamental Concepts of Regressions -Types of Regression Techniques -Examples with Datasets for Different Regression Techniques -Variable Selection in Regression Models -Regression Assumptions in Statistical and Machine Learning -Data Modelling -Considerations for Regression Algorithm Selection -Conclusion
Chapter 8:Data Modelling (Neural Networks) -Overview -Neural Networks as the Foundation of Deep Learning -Fundamental Architecture and Concepts -Types of Neural Networks -Neural Network Architectures in Supervised and Unsupervised Learning -Conclusion
Chapter 9: Data Modelling (Clustering) -Overview -Fundamental Concepts of Clustering -Data Points Assignments -Types of Clustering Techniques -Overview of Clustering Algorithms -Determining the Number of Clusters -Applying Clustering Concepts with Example Dataset -Feature Scaling in Clustering -Assessing Clustering Model Quality -Considerations in Selecting Clustering Techniques -Conclusion
Chapter 10: Data Modelling (Association Rules) -Overview -Fundamental Concepts of Association Rules -Types of Association Rules -Considerations in Selecting Association Rules Algorithms -Conclusion
Chapter 11: Data Modelling (Ensemble Models) -Overview -Fundamental Concepts of Ensemble Models -Architectures of Ensemble Models -Existing Algorithms and Implementations for Ensemble Models -Considerations in Selecting an Ensemble Architectural Strategy -Conclusion
Chapter 12: Model Evaluation -Overview -Model Fit and Generalisation -Model Performance -Model Complexity and Interpretability -Sampling Techniques for Model Validation -Feature Importance in Model Evaluation -Efficiency -Robustness -Additional Considerations for Model Deployment -Conclusion
Chapter 13: Data Mining Ethics and Emerging Considerations -Overview -Ethical Foundations in Data Mining -Ethical Data Mining Lifecycle -Ethics Guidelines, Policies and Law -Evolving Ethics in Data Mining -Ethical Challenges in Data Mining for Generative AI -Conclusion
Exercises
References
Index
About the Author :
Chua Hui Na is Professor at Sunway University, Malaysia, specialising in data mining, applied machine learning, and responsible AI. With extensive industry experience in data engineering and analytical systems development, she is involved in nationally funded and industry-driven research and development initiatives that have led to multiple intellectual property and copyright filings.
