Data Clustering: Theory, Algorithms, and Applications

Data Clustering: Theory, Algorithms, and Applications
  • eBook:
    Data Clustering: Theory, Algorithms, and Applications
  • Author:
    Guojun Gan, Chaoqun Ma, Jianhong Wu
  • Edition:
  • Categories:
  • Data:
    May 30, 2007
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    184 pages
  • Format:

Book Description
Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. This book starts with basic information on cluster analysis, including the classification of data and the corresponding similarity measures, followed by the presentation of over 50 clustering algorithms in groups according to some specific baseline methodologies such as hierarchical, centre-based, and search-based methods. As a result, readers and users can easily identify an appropriate algorithm for their applications and compare novel ideas with existing results. The book also provides examples of clustering applications to illustrate the advantages and shortcomings of different clustering architectures and algorithms. Application areas include pattern recognition, artificial intelligence, information technology, image processing, biology, psychology, and marketing. Suitable as a textbook for an introductory course in cluster analysis or as source material for a graduate-level introduction to data mining.


Part I Clustering, Data, and Similarity Measures
Chapter 1. Data Clustering
Chapter 2. Data Types
Chapter 3. Scale Conversion
Chapter 4. Data Standardization and Transformation
Chapter 5. Data Visualization
Chapter 6. Similarity and Dissimilarity Measures

Part II Clustering Algorithms
Chapter 7. Hierarchical Clustering Techniques
Chapter 8. Fuzzy Clustering Algorithms
Chapter 9. Center-based Clustering Algorithms
Chapter 10. Search-based Clustering Algorithms
Chapter 11. Graph-based Clustering Algorithms
Chapter 12. Grid-based Clustering Algorithms
Chapter 13. Density-based Clustering Algorithms
Chapter 14. Model-based Clustering Algorithms
Chapter 15. Subspace Clustering
Chapter 16. Miscellaneous Algorithms
Chapter 17. Evaluation of Clustering Algorithms

Part III Applications of Clustering
Chapter 18. Clustering Gene Expression Data

Part IV MATLAB and C++ for Clustering
Chapter 19. Data Clustering in MATLAB
Chapter 20. Clustering in C/C++

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