LNAI 3171 An Efficient Clustering Method for High Dimensional Data Mining 1st Edition by Jae Woo Chang, Yong Ki Kim ISBN 9783540206460 354020646X

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Product details:
ISBN 10: 354020646X
ISBN 13: 9783540206460
Author: Jae Woo Chang, Yong Ki Kim
 

Most clustering methods for data mining applications do not work efficiently when dealing with large, high-dimensional data. This is caused by so-called ‘curse of dimensionality’ and the limitation of available memory. In this paper, we propose an efficient clustering method for handling of large amounts of high-dimensional data. Our clustering method provides both an efficient cell creation and a cell insertion algorithm. To achieve good retrieval performance on clusters, we also propose a filtering-based index structure using an approximation technique. We compare the performance of our clustering method with the CLIQUE method. The experimental results show that our clustering method achieves better performance on cluster construction time and retrieval time.

LNAI 3171 An Efficient Clustering Method for High Dimensional Data Mining 1st Edition Table of contents:

Chapter 1: Introduction to Clustering and High-Dimensional Data Mining

• 1.1. What is Clustering?
• 1.2. The Importance of Clustering in Data Mining
• 1.3. Challenges of Clustering High-Dimensional Data
• 1.4. High-Dimensional Data and the Curse of Dimensionality
• 1.5. Key Goals and Contributions of the Book

Chapter 2: Fundamentals of Data Mining and Clustering

• 2.1. Introduction to Data Mining Concepts
• 2.2. Clustering Algorithms: Overview and Categories
• 2.3. Distance Measures for Clustering High-Dimensional Data
• 2.4. Clustering Evaluation Metrics and Techniques
• 2.5. Real-World Applications of Clustering

Chapter 3: The Curse of Dimensionality in Data Mining

• 3.1. What is the Curse of Dimensionality?
• 3.2. Challenges in High-Dimensional Data Clustering
• 3.3. Dimensionality Reduction Techniques
• 3.4. Feature Selection vs. Feature Extraction
• 3.5. Case Study: Overcoming Dimensionality in Text Mining

Chapter 4: Efficient Clustering Algorithms for High-Dimensional Data

• 4.1. Traditional Clustering Algorithms (K-Means, DBSCAN, etc.)
• 4.2. Limitations of Traditional Algorithms for High-Dimensional Data
• 4.3. Enhanced Clustering Techniques for High-Dimensional Data
• 4.4. Proposed Efficient Clustering Algorithm: Principles and Approach
• 4.5. Experimental Results: Algorithm Performance in High-Dimensional Spaces

Chapter 5: Dimensionality Reduction Techniques for High-Dimensional Clustering

• 5.1. The Role of Dimensionality Reduction in Clustering
• 5.2. Principal Component Analysis (PCA) and Its Variants
• 5.3. t-Distributed Stochastic Neighbor Embedding (t-SNE)
• 5.4. Linear Discriminant Analysis (LDA) and Other Methods
• 5.5. Case Study: Dimensionality Reduction in Image Clustering

Chapter 6: Hybrid Approaches: Combining Clustering and Dimensionality Reduction

• 6.1. Combining Dimensionality Reduction with Clustering
• 6.2. K-Means with PCA for High-Dimensional Clustering
• 6.3. Other Hybrid Clustering Techniques
• 6.4. Advantages and Challenges of Hybrid Approaches
• 6.5. Case Study: Using PCA for High-Dimensional Biological Data Clustering

Chapter 7: Advanced Clustering Methods for High-Dimensional Data

• 7.1. Spectral Clustering for High-Dimensional Data
• 7.2. Density-Based Clustering (DBSCAN and Variants)
• 7.3. Hierarchical Clustering in High-Dimensional Spaces
• 7.4. Deep Learning Approaches for Clustering High-Dimensional Data
• 7.5. Case Study: Clustering in Social Network Data

Chapter 8: Evaluation of Clustering Algorithms in High-Dimensional Spaces

• 8.1. Evaluation Metrics for Clustering Quality
• 8.2. Internal vs. External Evaluation Measures
• 8.3. Validating Clustering Results: Stability and Robustness
• 8.4. Real-World Evaluation: High-Dimensional Data Sets
• 8.5. Comparative Analysis: Traditional vs. Efficient High-Dimensional Clustering

Chapter 9: Applications of High-Dimensional Clustering

• 9.1. High-Dimensional Clustering in Bioinformatics and Genomics
• 9.2. Clustering in Text Mining and Natural Language Processing (NLP)
• 9.3. Image and Video Data Clustering
• 9.4. Clustering for Anomaly Detection in High-Dimensional Data
• 9.5. Case Study: Clustering in E-Commerce and Recommendation Systems

Chapter 10: Challenges and Future Directions in High-Dimensional Data Clustering

• 10.1. Scalability Issues in High-Dimensional Clustering
• 10.2. Handling Noise and Outliers in High-Dimensional Spaces
• 10.3. Future Trends in Clustering Algorithms and Techniques
• 10.4. The Role of Machine Learning and AI in Future Clustering Models
• 10.5. Summary and Future Research Directions

Chapter 11: Conclusion

• 11.1. Summary of Key Concepts in High-Dimensional Data Clustering
• 11.2. Importance of Efficient Clustering for Data Mining Applications
• 11.3. Final Thoughts and Future of High-Dimensional Clustering

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