Advances in Knowledge Discovery and Data Mining Part II 14th edition by Mohammed Zaki , Jeffrey Xu Yu, Ravindran, Vikram Pudi ISBN 3642136710 978-3642136719

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ISBN 10:  3642136710
ISBN 13: 978-3642136719
Author:  Mohammed Zaki , Jeffrey Xu Yu, Ravindran, Vikram Pudi 

presentations (15 mins). The total acceptance rate was thus about 23.5% across both categories.

Advances in Knowledge Discovery and Data Mining Part II 14th  Table of contents:

Preface

• Overview of Advances in Knowledge Discovery and Data Mining
• Scope of Part II
• Acknowledgments

Chapter 1: Advanced Data Mining Techniques

• Deep Learning for Knowledge Discovery
• Advanced Clustering Methods
• High-Dimensional Data Mining
• Techniques for Unstructured Data

Chapter 2: Data Preprocessing and Feature Engineering

• Handling Missing Data and Outliers
• Dimensionality Reduction Techniques
• Feature Selection and Extraction Methods
• Data Transformation and Normalization

Chapter 3: Machine Learning for Knowledge Discovery

• Supervised Learning: Advanced Algorithms and Applications
• Unsupervised Learning and Clustering
• Reinforcement Learning for Data Mining
• Semi-supervised Learning and Transfer Learning

Chapter 4: Data Mining for Big Data

• Techniques for Scalability in Big Data Mining
• Distributed Data Mining and Cloud Computing
• Mining Data Streams
• Real-Time Data Analytics

Chapter 5: Knowledge Representation and Visualization

• Visual Analytics for Knowledge Discovery
• Knowledge Representation Models
• Visualizing Large-Scale Data
• Interactive Data Exploration Tools

Chapter 6: Pattern Recognition and Data Mining Applications

• Advanced Pattern Discovery Algorithms
• Association Rule Mining and Applications
• Sequence Mining in Text and Time-Series Data
• Anomaly Detection and Outlier Mining

Chapter 7: Data Mining in Industry and Real-World Applications

• Applications in Healthcare: Medical Data Mining
• Data Mining in Finance and Fraud Detection
• Data Mining in Marketing and Customer Analytics
• Applications in Security and Surveillance

Chapter 8: Ethical Issues and Data Mining

• Privacy Concerns in Data Mining
• Bias and Fairness in Algorithms
• Regulatory and Legal Challenges
• Ethical Implications of Knowledge Discovery

Chapter 9: Future Directions in Knowledge Discovery and Data Mining

• Trends in AI and Knowledge Discovery
• Emerging Technologies for Data Mining (e.g., Quantum Computing)
• The Role of Data Mining in Autonomous Systems
• Challenges in Integrating AI with Knowledge Discovery

Chapter 10: Case Studies and Best Practices

• Case Study 1: Data Mining in Healthcare Systems
• Case Study 2: Real-Time Analytics in Social Media
• Case Study 3: Data Mining for Predictive Maintenance in Manufacturing
• Lessons Learned and Key Takeaways

Index

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