Data Clustering in C++

Data Clustering in C++

by Guojun Gan
Released March 2011
Publisher(s): Chapman and Hall/CRC
ISBN: 9781439862247

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Book description

Data clustering is a highly interdisciplinary field, the goal of which is to divide a set of objects into homogeneous groups such that objects in the same group are similar and objects in different groups are quite distinct. Thousands of theoretical papers and a number of books on data clustering have been published over the past 50 years. However,

Table of contents

  1. Front Cover
  2. Dedication
  3. Contents (1/2)
  4. Contents (2/2)
  5. List of Figures
  6. List of Tables
  7. Preface
  8. I. Data Clustering and C++ Preliminaries
    1. 1. Introduction to Data Clustering (1/6)
    2. 1. Introduction to Data Clustering (2/6)
    3. 1. Introduction to Data Clustering (3/6)
    4. 1. Introduction to Data Clustering (4/6)
    5. 1. Introduction to Data Clustering (5/6)
    6. 1. Introduction to Data Clustering (6/6)
    7. 2. The Unified Modeling Language (1/3)
    8. 2. The Unified Modeling Language (2/3)
    9. 2. The Unified Modeling Language (3/3)
    10. 3. Object-Oriented Programming and C++ (1/4)
    11. 3. Object-Oriented Programming and C++ (2/4)
    12. 3. Object-Oriented Programming and C++ (3/4)
    13. 3. Object-Oriented Programming and C++ (4/4)
    14. 4. DesignPatterns (1/4)
    15. 4. DesignPatterns (2/4)
    16. 4. DesignPatterns (3/4)
    17. 4. DesignPatterns (4/4)
    18. 5. C++ Libraries and Tools (1/5)
    19. 5. C++ Libraries and Tools (2/5)
    20. 5. C++ Libraries and Tools (3/5)
    21. 5. C++ Libraries and Tools (4/5)
    22. 5. C++ Libraries and Tools (5/5)
  9. II. A C++ Data Clustering Framework
    1. 6. The Clustering Library (1/3)
    2. 6. The Clustering Library (2/3)
    3. 6. The Clustering Library (3/3)
    4. 7. Datasets (1/4)
    5. 7. Datasets (2/4)
    6. 7. Datasets (3/4)
    7. 7. Datasets (4/4)
    8. 8. Clusters (1/2)
    9. 8. Clusters (2/2)
    10. 9. Dissimilarity Measures (1/2)
    11. 9. Dissimilarity Measures (2/2)
    12. 10. Clustering Algorithms (1/3)
    13. 10. Clustering Algorithms (2/3)
    14. 10. Clustering Algorithms (3/3)
    15. 11. Utility Classes (1/5)
    16. 11. Utility Classes (2/5)
    17. 11. Utility Classes (3/5)
    18. 11. Utility Classes (4/5)
    19. 11. Utility Classes (5/5)
  10. III. Data Clustering Algorithms
    1. 12. Agglomerative Hierarchical Algorithms (1/7)
    2. 12. Agglomerative Hierarchical Algorithms (2/7)
    3. 12. Agglomerative Hierarchical Algorithms (3/7)
    4. 12. Agglomerative Hierarchical Algorithms (4/7)
    5. 12. Agglomerative Hierarchical Algorithms (5/7)
    6. 12. Agglomerative Hierarchical Algorithms (6/7)
    7. 12. Agglomerative Hierarchical Algorithms (7/7)
    8. 13. DIANA (1/3)
    9. 13. DIANA (2/3)
    10. 13. DIANA (3/3)
    11. 14. The k-means Algorithm (1/3)
    12. 14. The k-means Algorithm (2/3)
    13. 14. The k-means Algorithm (3/3)
    14. 15. The c-means Algorithm (1/3)
    15. 15. The c-means Algorithm (2/3)
    16. 15. The c-means Algorithm (3/3)
    17. 16. The k-prototypes Algorithm (1/2)
    18. 16. The k-prototypes Algorithm (2/2)
    19. 17. The Genetic k-modes Algorithm (1/3)
    20. 17. The Genetic k-modes Algorithm (2/3)
    21. 17. The Genetic k-modes Algorithm (3/3)
    22. 18. The FSC Algorithm (1/3)
    23. 18. The FSC Algorithm (2/3)
    24. 18. The FSC Algorithm (3/3)
    25. 19. The Gaussian Mixture Algorithm (1/4)
    26. 19. The Gaussian Mixture Algorithm (2/4)
    27. 19. The Gaussian Mixture Algorithm (3/4)
    28. 19. The Gaussian Mixture Algorithm (4/4)
    29. 20. A Parallel k-means Algorithm (1/4)
    30. 20. A Parallel k-means Algorithm (2/4)
    31. 20. A Parallel k-means Algorithm (3/4)
    32. 20. A Parallel k-means Algorithm (4/4)
  11. A. Exercises and Projects
  12. B. Listings (1/28)
  13. B. Listings (2/28)
  14. B. Listings (3/28)
  15. B. Listings (4/28)
  16. B. Listings (5/28)
  17. B. Listings (6/28)
  18. B. Listings (7/28)
  19. B. Listings (8/28)
  20. B. Listings (9/28)
  21. B. Listings (10/28)
  22. B. Listings (11/28)
  23. B. Listings (12/28)
  24. B. Listings (13/28)
  25. B. Listings (14/28)
  26. B. Listings (15/28)
  27. B. Listings (16/28)
  28. B. Listings (17/28)
  29. B. Listings (18/28)
  30. B. Listings (19/28)
  31. B. Listings (20/28)
  32. B. Listings (21/28)
  33. B. Listings (22/28)
  34. B. Listings (23/28)
  35. B. Listings (24/28)
  36. B. Listings (25/28)
  37. B. Listings (26/28)
  38. B. Listings (27/28)
  39. B. Listings (28/28)
  40. C. Software (1/2)
  41. C. Software (2/2)
  42. Bibliography (1/4)
  43. Bibliography (2/4)
  44. Bibliography (3/4)
  45. Bibliography (4/4)

Product information

  • Title: Data Clustering in C++
  • Author(s): Guojun Gan
  • Release date: March 2011
  • Publisher(s): Chapman and Hall/CRC
  • ISBN: 9781439862247