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Data Clustering in C++

Guojun Gan
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A01=Guojun Gan
Agglomerative Hierarchical Clustering Algorithms
algorithm implementation guide
Algorithm Multiple Times
algorithms
attributes
Author_Guojun Gan
Boost Libraries
C++ clustering framework development
categorical
Category=PBT
Category=UFM
Category=UMN
Category=UN
Category=UNC
Category=UNF
Category=UY
centers
Cluster Centers
Cluster Id
Clustering Algorithm
Const Boost
cross
Da Ta
Data
Data Clustering Algorithms
Data Members
Data Set
data visualization tools
Dataset
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
function
Fuzzy Clustering Algorithms
Hierarchical Clustering Algorithms
Initial Cluster Centers
Iris Dataset
machine learning techniques
member
Member Functions
Namespace Std
object oriented programming
Private Data Member
Public Member Functions
pure
Pure Virtual Function
statistical pattern recognition
Subspace Clusters
tabulation
UML Class Diagram
unsupervised learning methods
virtual
Virtual Destructor

Product details

  • ISBN 9781439862230
  • Weight: 907g
  • Dimensions: 156 x 234mm
  • Publication Date: 28 Mar 2011
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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, 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 downloadable resources. The only requirements to compile the code are a modern C++ compiler and the Boost C++ libraries.

About Guojun Gan

Guojun Gan, Manulife Financial, Toronto, Canada

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