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Book Description

Data mining has witnessed substantial advances in recent decades. New research questions and practical challenges have arisen from emerging areas and applications within the various fields closely related to human daily life, e.g. social media and social networking. This book aims to bridge the gap between traditional data mining and the latest advances in newly emerging information services. It explores the extension of well-studied algorithms and approaches into these new research arenas.

Table of Contents

  1. Front Cover
  2. Preface
  3. Contents (1/2)
  4. Contents (2/2)
  5. Part I: Fundamentals
    1. Chapter 1: Introduction (1/4)
    2. Chapter 1: Introduction (2/4)
    3. Chapter 1: Introduction (3/4)
    4. Chapter 1: Introduction (4/4)
    5. Chapter 2: Mathematical Foundations (1/5)
    6. Chapter 2: Mathematical Foundations (2/5)
    7. Chapter 2: Mathematical Foundations (3/5)
    8. Chapter 2: Mathematical Foundations (4/5)
    9. Chapter 2: Mathematical Foundations (5/5)
    10. Chapter 3: Data Preparation (1/3)
    11. Chapter 3: Data Preparation (2/3)
    12. Chapter 3: Data Preparation (3/3)
    13. Chapter 4: Clustering Analysis (1/9)
    14. Chapter 4: Clustering Analysis (2/9)
    15. Chapter 4: Clustering Analysis (3/9)
    16. Chapter 4: Clustering Analysis (4/9)
    17. Chapter 4: Clustering Analysis (5/9)
    18. Chapter 4: Clustering Analysis (6/9)
    19. Chapter 4: Clustering Analysis (7/9)
    20. Chapter 4: Clustering Analysis (8/9)
    21. Chapter 4: Clustering Analysis (9/9)
    22. Chapter 5: Classification (1/4)
    23. Chapter 5: Classification (2/4)
    24. Chapter 5: Classification (3/4)
    25. Chapter 5: Classification (4/4)
    26. Chapter 6: Frequent Pattern Mining (1/7)
    27. Chapter 6: Frequent Pattern Mining (2/7)
    28. Chapter 6: Frequent Pattern Mining (3/7)
    29. Chapter 6: Frequent Pattern Mining (4/7)
    30. Chapter 6: Frequent Pattern Mining (5/7)
    31. Chapter 6: Frequent Pattern Mining (6/7)
    32. Chapter 6: Frequent Pattern Mining (7/7)
  6. Part II: Advanced Data Mining
    1. Chapter 7: Advanced Clustering Analysis (1/6)
    2. Chapter 7: Advanced Clustering Analysis (2/6)
    3. Chapter 7: Advanced Clustering Analysis (3/6)
    4. Chapter 7: Advanced Clustering Analysis (4/6)
    5. Chapter 7: Advanced Clustering Analysis (5/6)
    6. Chapter 7: Advanced Clustering Analysis (6/6)
    7. Chapter 8: Multi-Label Classification (1/5)
    8. Chapter 8: Multi-Label Classification (2/5)
    9. Chapter 8: Multi-Label Classification (3/5)
    10. Chapter 8: Multi-Label Classification (4/5)
    11. Chapter 8: Multi-Label Classification (5/5)
    12. Chapter 9: Privacy Preserving in Data Mining (1/2)
    13. Chapter 9: Privacy Preserving in Data Mining (2/2)
  7. Part III: Emerging Applications
    1. Chapter 10: Data Stream (1/5)
    2. Chapter 10: Data Stream (2/5)
    3. Chapter 10: Data Stream (3/5)
    4. Chapter 10: Data Stream (4/5)
    5. Chapter 10: Data Stream (5/5)
    6. Chapter 11: Recommendation Systems (1/3)
    7. Chapter 11: Recommendation Systems (2/3)
    8. Chapter 11: Recommendation Systems (3/3)
    9. Chapter 12: Social Tagging Systems (1/5)
    10. Chapter 12: Social Tagging Systems (2/5)
    11. Chapter 12: Social Tagging Systems (3/5)
    12. Chapter 12: Social Tagging Systems (4/5)
    13. Chapter 12: Social Tagging Systems (5/5)
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