Knowledge Discovery from Data Streams

Knowledge Discovery from Data Streams

by Joao Gama
Released May 2010
Publisher(s): Chapman and Hall/CRC
ISBN: 9781439826126

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Book description

Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents

Table of contents

  1. Front cover
  2. Contents (1/2)
  3. Contents (2/2)
  4. List of Tables
  5. List of Figures
  6. List of Algorithms
  7. Foreword
  8. Acknowledgments
  9. Chapter 1: Knowledge Discovery from Data Streams (1/2)
  10. Chapter 1: Knowledge Discovery from Data Streams (2/2)
  11. Chapter 2: Introduction to Data Streams (1/6)
  12. Chapter 2: Introduction to Data Streams (2/6)
  13. Chapter 2: Introduction to Data Streams (3/6)
  14. Chapter 2: Introduction to Data Streams (4/6)
  15. Chapter 2: Introduction to Data Streams (5/6)
  16. Chapter 2: Introduction to Data Streams (6/6)
  17. Chapter 3: Change Detection (1/4)
  18. Chapter 3: Change Detection (2/4)
  19. Chapter 3: Change Detection (3/4)
  20. Chapter 3: Change Detection (4/4)
  21. Chapter 4: Maintaining Histograms from Data Streams (1/3)
  22. Chapter 4: Maintaining Histograms from Data Streams (2/3)
  23. Chapter 4: Maintaining Histograms from Data Streams (3/3)
  24. Chapter 5: Evaluating Streaming Algorithms (1/4)
  25. Chapter 5: Evaluating Streaming Algorithms (2/4)
  26. Chapter 5: Evaluating Streaming Algorithms (3/4)
  27. Chapter 5: Evaluating Streaming Algorithms (4/4)
  28. Chapter 6: Clustering from Data Streams (1/4)
  29. Chapter 6: Clustering from Data Streams (2/4)
  30. Chapter 6: Clustering from Data Streams (3/4)
  31. Chapter 6: Clustering from Data Streams (4/4)
  32. Chapter 7: Frequent Pattern Mining (1/4)
  33. Chapter 7: Frequent Pattern Mining (2/4)
  34. Chapter 7: Frequent Pattern Mining (3/4)
  35. Chapter 7: Frequent Pattern Mining (4/4)
  36. Chapter 8: Decision Trees from Data Streams (1/4)
  37. Chapter 8: Decision Trees from Data Streams (2/4)
  38. Chapter 8: Decision Trees from Data Streams (3/4)
  39. Chapter 8: Decision Trees from Data Streams (4/4)
  40. Chapter 9: Novelty Detection in Data Streams (1/4)
  41. Chapter 9: Novelty Detection in Data Streams (2/4)
  42. Chapter 9: Novelty Detection in Data Streams (3/4)
  43. Chapter 9: Novelty Detection in Data Streams (4/4)
  44. Chapter 10: Ensembles of Classiers (1/3)
  45. Chapter 10: Ensembles of Classiers (2/3)
  46. Chapter 10: Ensembles of Classiers (3/3)
  47. Chapter 11: Time Series Data Streams (1/4)
  48. Chapter 11: Time Series Data Streams (2/4)
  49. Chapter 11: Time Series Data Streams (3/4)
  50. Chapter 11: Time Series Data Streams (4/4)
  51. Chapter 12: Ubiquitous Data Mining (1/4)
  52. Chapter 12: Ubiquitous Data Mining (2/4)
  53. Chapter 12: Ubiquitous Data Mining (3/4)
  54. Chapter 12: Ubiquitous Data Mining (4/4)
  55. Chapter 13: Final Comments
  56. Appendix A
  57. Bibliography (1/5)
  58. Bibliography (2/5)
  59. Bibliography (3/5)
  60. Bibliography (4/5)
  61. Bibliography (5/5)
  62. Back cover

Product information

  • Title: Knowledge Discovery from Data Streams
  • Author(s): Joao Gama
  • Release date: May 2010
  • Publisher(s): Chapman and Hall/CRC
  • ISBN: 9781439826126