C++实现数据聚类：从理论到实践的全面指南

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"《C++中的数据聚类：面向对象的方法》是一本深入讲解数据聚类技术在C++编程中的应用教程。作者结合多年在SGI和Adobe教授编程课程的经验，回顾了过去几十年间数据聚类领域的理论发展。数据聚类是一个跨学科的领域，其核心目标是将一组对象划分为相似的组别，确保同一组内的对象相似，而不同组之间的差异明显。在过去的五十年里，关于数据聚类的研究文献数量众多，但大多数聚焦于理论探讨，而非实际算法实现。本书填补了这一空白，提供了一种面向对象的C++实践方法，帮助读者理解和掌握如何在实际项目中应用数据聚类算法。作者列举了一系列相关书籍，如《理解复杂数据集：矩阵分解的数据挖掘》、《计算特征选择方法》、《约束聚类：算法、理论与应用》等，这些书籍分别涵盖了数据挖掘、特征选择、反恐和执法领域的知识，以及多媒体数据挖掘、文本挖掘、生物数据挖掘等多方面的实践指南。此外，书单中还提到《下一代数据挖掘》、《数据挖掘设计与营销》以及《数据挖掘顶级算法》等，展示了数据聚类在各种应用场景中的重要性，如地理数据分析、文本分类和聚类，以及商业决策中的应用。这些著作不仅介绍了理论，也提供了实用的工具和技术，以便读者在C++环境中有效地进行数据聚类分析。通过学习这些资源，读者不仅可以深入了解数据聚类的基本原理，还能掌握如何用C++语言编写高效的聚类算法，并将其应用于实际问题解决中。这对于从事数据科学、机器学习或软件开发的专业人士来说，是一份宝贵的参考资料，有助于提升他们在处理大规模、复杂数据集时的技能和效率。"

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6.1 The directory structure of the clustering library. . . . . . . . 104

7.1 Class diagram of attributes. . . . . . . . . . . . . . . . . . . 116

7.2 Classdiagramofrecords..................... 123

7.3 Class diagram of Dataset.................... 125

8.1 Hierarchyofclusterclasses. .................. 132

8.2 Ahierarchicaltreewithlevels.................. 136

10.1 Class diagram of algorithm classes. . . . . . . . . . . . . . . 153

11.1 A generated dataset with 9 points. . . . . . . . . . . . . . . 174

11.2 AnEPSﬁgure. ......................... 177

11.3 A dendrogram that shows 100 nodes. . . . . . . . . . . . . . 181

11.4 A dendrogram that shows 50 nodes. . . . . . . . . . . . . . 182

12.1 Class diagram of agglomerative hierarchical algorithms. . . 188

12.2 The dendrogram produced by applying the single linkage al-

gorithmtotheIrisdataset. .................. 199

12.3 The dendrogram produced by applying the single linkage al-

gorithmtothesyntheticdataset. ............... 200

12.4 The dendrogram produced by applying the complete linkage

algorithm to the Iris dataset. . . . . . . . . . . . . . . . . . 201

12.5 The dendrogram produced by applying the complete linkage

algorithm to the synthetic dataset. . . . . . . . . . . . . . . 203

12.6 The dendrogram produced by applying the group average al-

gorithmtotheIrisdataset. .................. 204

12.7 The dendrogram produced by applying the group average al-

gorithmtothesyntheticdataset. ............... 205

12.8 The dendrogram produced by applying the weighted group

average algorithm to the Iris dataset. . . . . . . . . . . . . . 206

12.9 The dendrogram produced by applying the weighted group

average algorithm to the synthetic dataset. . . . . . . . . . . 207

12.10 The dendrogram produced by applying the centroid algorithm

totheIrisdataset. ....................... 208

12.11 The dendrogram produced by applying the centroid algorithm

to the synthetic dataset. . . . . . . . . . . . . . . . . . . . . 209

12.12 The dendrogram produced by applying the median algorithm

totheIrisdataset. ....................... 211

12.13 The dendrogram produced by applying the median algorithm

to the synthetic dataset. . . . . . . . . . . . . . . . . . . . . 212

12.14 The dendrogram produced by applying the ward algorithm

totheIrisdataset. ....................... 213

12.15 The dendrogram produced by applying Ward’s algorithm to

thesyntheticdataset....................... 214

Preface

Data clustering is a highly interdisciplinary ﬁeld whose goal 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

papers and a number of books on data clustering have been published over

the past 50 years. However, almost all papers and books focus on the theory

of data clustering. There are few books that teach people how to implement

data clustering algorithms.

This book was written for anyone who wants to implement data clustering

algorithms and for those who want to implement new data clustering algo-

rithms in a better way. Using object-oriented design and programming tech-

niques, I have exploited 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 me through the develop-

ment of the base data clustering classes and several popular data clustering

algorithms.

This book focuses on how to implement data clustering algorithms in an

object-oriented way. Other topics of clustering such as data pre-processing,

data visualization, cluster visualization, and cluster interpretation are touched

but not in detail. In this book, I used a direct and simple way to implement

data clustering algorithms so that readers can understand the methodology

easily. I also present the material in this book in a straightforward way. When

I introduce a class, I present and explain the class method by method rather

than present and go through the whole implementation of the class.

Complete listings of classes, examples, unit test cases, and GNU conﬁg-

uration ﬁles are included in the appendices of this book as well as in the

CD-ROM of the book. I have tested the code under Unix-like platforms (e.g.,

Ubuntu and Cygwin) and Microsoft Windows XP. The only requirements to

compile the code are a modern C++ compiler and the Boost C++ libraries.

This book is divided into three parts: Data Clustering and C++ Prelimi-

naries, A C++ Data Clustering Framework, and Data Clustering Algorithms.

The ﬁrst part reviews some basic concepts of data clustering, the uniﬁed

modeling language, object-oriented programming in C++, and design pat-

terns. The second part develops the data clustering base classes. The third

part implements several popular data clustering algorithms. The content of

each chapter is described brieﬂy below.

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