A Structured Approach to Data Mining

A Structured Approach to Data Mining offers a systematic introduction, emphasizing conceptual understanding, methodological reasoning, and technique application.

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