LNAI 3755 Data Mining Theory Methodology Techniques, and Applications 1st edition by Graham Williams, Simeon Simoff ISBN 3540325476 978-3540325475

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ISBN 10: 3540325476
ISBN 13: 978-3540325475
Author: Graham Williams, Simeon Simoff 

This volume provides a snapshot of the current state of the art in data mining, presenting it both in terms of technical developments and industrial applications. The collection of chapters is based on works presented at the Australasian Data Mining conferences and industrial forums. Authors include some of Australia’s leading researchers and practitioners in data mining. The volume also contains chapters by regional and international authors.

LNAI 3755 – Data Mining Theory, Methodology, Techniques, and Applications 1st Table of contents:

Preface

• Introduction to Data Mining
• Overview of the Book’s Structure
• Target Audience and How to Use the Book

Chapter 1: Introduction to Data Mining

• Definition and History of Data Mining
• Data Mining vs. Traditional Data Analysis
• Data Mining Process and Lifecycle
• Key Challenges and Trends in Data Mining

Chapter 2: Data Preprocessing and Cleaning

• Importance of Data Quality in Mining
• Handling Missing Data
• Data Transformation and Normalization
• Feature Selection and Engineering
• Data Reduction Techniques

Chapter 3: Data Mining Techniques

• Classification Techniques (e.g., Decision Trees, SVM, KNN)
• Clustering Techniques (e.g., K-Means, Hierarchical Clustering)
• Association Rule Mining (e.g., Apriori Algorithm)
• Regression Analysis
• Anomaly Detection

Chapter 4: Model Evaluation and Validation

• Performance Metrics for Data Mining Models
• Cross-Validation and Holdout Methods
• Overfitting and Model Complexity
• ROC Curves and AUC
• Model Selection and Comparison

Chapter 5: Advanced Data Mining Methods

• Ensemble Learning Methods (e.g., Random Forests, Boosting)
• Neural Networks and Deep Learning in Data Mining
• Dimensionality Reduction Techniques (e.g., PCA, LDA)
• Text Mining and Natural Language Processing
• Time Series Analysis and Forecasting

Chapter 6: Data Mining Tools and Software

• Overview of Popular Data Mining Tools (e.g., Weka, KNIME, R, Python)
• Visualization Tools for Data Mining
• Cloud-Based Data Mining Services
• Hands-On Approach to Using Data Mining Tools

Chapter 7: Applications of Data Mining

• Data Mining in Business Intelligence
• Healthcare Applications (e.g., Disease Prediction, Drug Discovery)
• Data Mining in Marketing and Customer Segmentation
• Fraud Detection and Risk Management
• Applications in Social Media and Web Mining

Chapter 8: Big Data and Data Mining

• Big Data Challenges and Technologies
• Mining Large-Scale Datasets
• Hadoop and MapReduce for Data Mining
• Scalable Algorithms for Big Data
• Case Studies of Big Data Applications

Chapter 9: Ethical Issues in Data Mining

• Privacy Concerns in Data Mining
• Ethical Implications of Predictive Modeling
• Bias and Fairness in Data Mining Algorithms
• Data Mining and Legal Considerations

Chapter 10: Future Directions in Data Mining

• Emerging Trends and Technologies in Data Mining
• The Role of Artificial Intelligence and Machine Learning
• Data Mining in the Internet of Things (IoT)
• Integration of Data Mining with Other Domains (e.g., AI, Robotics)

Appendices

• Glossary of Terms
• Further Reading and Resources
• Index

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