Franklin

Data Clustering in C++ : An Object-Oriented Approach.

Author/Creator:
Gan, Guojun.
Publication:
London : CRC Press LLC, 2011.
Format/Description:
Book
1 online resource (512 pages)
Edition:
1st ed.
Series:
Chapman and Hall/CRC Data Mining and Knowledge Discovery Ser.
Chapman and Hall/CRC Data Mining and Knowledge Discovery Ser.
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Subjects:
Cluster analysis -- Data processing.
Form/Genre:
Electronic books.
Summary:
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, few books exist to teach people how to implement data clustering algorithms. This book was written for anyone who wants to implement or improve their data clustering algorithms. Using object-oriented design and programming techniques, Data Clustering in C++ exploits the commonalities of all data clustering algorithms to create a flexible set of reusable classes that simplifies the implementation of any data clustering algorithm. Readers can follow the development of the base data clustering classes and several popular data clustering algorithms. Additional topics such as data pre-processing, data visualization, cluster visualization, and cluster interpretation are briefly covered. This book is divided into three parts-- Data Clustering and C++ Preliminaries: A review of basic concepts of data clustering, the unified modeling language, object-oriented programming in C++, and design patterns A C++ Data Clustering Framework: The development of data clustering base classes Data Clustering Algorithms: The implementation of several popular data clustering algorithms A key to learning a clustering algorithm is to implement and experiment the clustering algorithm. Complete listings of classes, examples, unit test cases, and GNU configuration files are included in the appendices of this book as well as in the CD-ROM of the book. The only requirements to compile the code are a modern C++ compiler and the Boost C++ libraries.
Contents:
Front Cover
Dedication
Contents
List of Figures
List of Tables
Preface
I. Data Clustering and C++ Preliminaries
1. Introduction to Data Clustering
2. The Unified Modeling Language
3. Object-Oriented Programming and C++
4. DesignPatterns
5. C++ Libraries and Tools
II. A C++ Data Clustering Framework
6. The Clustering Library
7. Datasets
8. Clusters
9. Dissimilarity Measures
10. Clustering Algorithms
11. Utility Classes
III. Data Clustering Algorithms
12. Agglomerative Hierarchical Algorithms
13. DIANA
14. The k-means Algorithm
15. The c-means Algorithm
16. The k-prototypes Algorithm
17. The Genetic k-modes Algorithm
18. The FSC Algorithm
19. The Gaussian Mixture Algorithm
20. A Parallel k-means Algorithm
A. Exercises and Projects
B. Listings
C. Software
Bibliography.
Notes:
Description based on publisher supplied metadata and other sources.
Local notes:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2021. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Other format:
Print version: Gan, Guojun Data Clustering in C++
ISBN:
9781439862247
9781439862230
OCLC:
740901290