Gan Data Clustering in C++

An Object-Oriented Approach
Erscheinungsjahr 2011
ISBN: 978-1-4398-6224-7
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 520 Seiten

Reihe: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

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.

Zielgruppe

Researchers and graduate students in statistics, computer science, and related areas.

Autoren/Hrsg.

Gan, Guojun

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Objektorientierte Programmierung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

Dedication
Preface
List of Tables
List of Figures

I Data Clustering and C++ Preliminaries
Introduction to Data Clustering
Data Clustering
Data Types

Dissimilarity and SimilarityMeasures
Hierarchical Clustering Algorithms
Partitional Clustering Algorithms
Cluster Validity
Clustering Applications
Literature of Clustering Algorithms

Summary
The Unified Modeling Language
PackageDiagrams

Class Diagrams
Use Case Diagrams
Activity Diagrams

Notes
Summary
Object-Oriented Programming and C++

Object-Oriented Programming
The C++ Programming Language
Encapsulation

Inheritance

Polymorphism

Exception Handling
Summary
Design Patterns
Singleton
Composite

Prototype
Strategy
TemplateMethod

Visitor

Summary
C++ Libraries and Tools
The Standard Template Library

Boost C++ Libraries

GNU Build System
Cygwin

Summary

II A C++ Data Clustering Framework
The Clustering Library
Directory Structure and Filenames
Specification Files

Macros and typedef Declarations

Error Handling

Unit Testing
Compilation and Installation

Summary
Datasets
Attributes

Records

Datasets
A Dataset Example
Summary
Clusters
Clusters

Partitional Clustering

Hierarchical Clustering
Summary
Dissimilarity Measures
The Distance Base Class

Minkowski Distance
Euclidean Distance
SimpleMatching Distance
Mixed Distance
Mahalanobis Distance

Summary
Clustering Algorithms
Arguments

Results

Algorithms

A Dummy Clustering Algorithm

Summary
Utility Classes
The Container Class
The Double-keyMap Class
The Dataset Adapters

The Node Visitors

The Dendrogram Class
The DendrogramVisitor

Summary

III Data Clustering Algorithms
Agglomerative Hierarchical Algorithms
Description of the Algorithm

Implementation
Examples
Summary
DIANA
Description of the Algorithm

Implementation
Examples
Summary
The k-means Algorithm
Description of the Algorithm

Implementation
Examples
Summary
The c-means Algorithm

Description of the Algorithm

Implementaion

Examples
Summary
The k-prototypes Algorithm
Description of the Algorithm

Implementation
Examples
Summary
The Genetic k-modes Algorithm
Description of the Algorithm

Implementation
Examples
Summary
The FSC Algorithm
Description of the Algorithm

Implementation
Examples
Summary
The Gaussian Mixture Algorithm
Description of the Algorithm

Implementation
Examples
Summary
A Parallel k-means Algorithm
Message Passing Interface

Description of the Algorithm

Implementation
Examples
Summary

A Exercises and Projects
B Listings
C Software
Bibliography
Author Index
Subject Index

Über Autor(innen)

Guojun Gan, Manulife Financial, Toronto, Canada