R语言数据挖掘：案例研究与2010年深度解析

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《数据挖掘与R学习：案例研究》（Data Mining with R: Learning with Case Studies, Nov. 2010）是一本由Chapman & Hall/CRC出版的数据挖掘和知识发现系列书籍。该书深入探讨了R语言在数据挖掘领域的应用，R是一种广泛使用的免费编程环境，特别适合于统计计算和图形展示，因其强大的数据分析功能而备受推崇。本书分为多个章节，涵盖了不同的数据挖掘主题和技术： 1. **理解复杂数据集：使用矩阵分解进行数据挖掘** - David Skillicorn介绍了如何利用矩阵分解技术来处理和解析复杂的数据集，这是一种关键的数学工具，有助于降维和特征提取。 2. **计算特征选择方法** - Huan Liu和Hiroshi Motoda讲解了如何通过计算方法优化数据集中的特征，以提高模型的效率和预测准确性。 3. **约束聚类：算法、理论与应用的最新进展** - Sugato Basu、Ian Davidson和Kiri L. Wagstaff探讨了聚类分析中的约束条件及其对实际问题的影响，包括算法改进和理论发展。 4. **反恐与执法中的知识发现** - David Skillicorn展示了数据挖掘在打击犯罪和维护社会安全方面的实用应用。 5. **多模态数据挖掘：概念与理论介绍** - Zhongfei Zhang和Ruofei Zhang着重于不同数据源（如文本、图像和视频）的整合，提供了系统性的理解和理论框架。 6. **下一代数据挖掘：趋势、领导者及未来方向** - Hillol Kargupta、Jiawei Han等人讨论了当前数据挖掘技术的前沿进展，以及它们对未来的影响。 7. **设计与营销中的数据挖掘** - Yukio Ohsawa和Katsutoshi Yada关注数据挖掘如何在商业决策中发挥作用，特别是设计和市场营销策略的制定。 8. **数据挖掘中的十大算法** - Xindong Wu和Vipin Kumar概述了数据挖掘领域中最常用和有效的算法，帮助读者理解和掌握核心方法。 9. **地理数据挖掘与知识发现** - Harvey J. Miller和Jiawei Han深入地理信息系统的数据挖掘，强调地理位置对知识发现的重要性。 10. **文本挖掘：分类、聚类及应用** - Ashok N. Srivastava和Mehran Sahami研究文本数据的处理和分析，如文档分类和主题模型。 11. **生物数据挖掘** - Jake Y. Chen和Stefano Lonardi探索生物学领域中的数据挖掘，如基因表达数据和蛋白质结构分析。 12. **电子健康记录中的信息发现** - Vagelis Hristos涉及医疗保健领域，讨论如何利用电子健康记录进行深度数据挖掘以提升医疗服务质量。这本书不仅提供了丰富的理论知识，还通过实例和案例研究让读者掌握R语言在数据挖掘中的实践应用，是数据科学家、分析师和机器学习工程师的宝贵参考资料。

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Chapter 1

Introduction

R is a programming language and an environment for statistical computing.

It is similar to the S language developed at AT&T Bell Laboratories by Rick

Becker, John Chambers and Allan Wilks. There are versions of R for the Unix,

Windows and Mac families of operating systems. Moreover, R runs on diﬀerent

computer architectures like Intel, PowerPC, Alpha systems and Sparc systems.

R was initially developed by Ihaka and Gentleman (1996), both from the Uni-

versity of Auckland, New Zealand. The current development of R is carried

out by a core team of a dozen people from diﬀerent institutions around the

world. R development takes advantage of a growing community that cooper-

ates in its development due to its open source philosophy. In eﬀect, the source

code of every R component is freely available for inspection and/or adapta-

tion. This fact allows you to check and test the reliability of anything you use

in R. There are many critics to the open source model. Most of them mention

the lack of support as one of the main drawbacks of open source software. It

is certainly not the case with R! There are many excellent documents, books

and sites that provide free information on R. Moreover, the excellent R-help

mailing list is a source of invaluable advice and information, much better than

any amount of money could ever buy! There are also searchable mailing lists

archives that you can (and should!) use before posting a question. More infor-

mation on these mailing lists can be obtained at the R Web site in the section

“Mailing Lists”.

Data mining has to do with the discovery of useful, valid, unexpected,

and understandable knowledge from data. These general objectives are obvi-

ously shared by other disciplines like statistics, machine learning, or pattern

recognition. One of the most important distinguishing issues in data mining

is size. With the widespread use of computer technology and information sys-

tems, the amount of data available for exploration has increased exponentially.

This poses diﬃcult challenges to the standard data analysis disciplines: One

has to consider issues like computational eﬃciency, limited memory resources,

interfaces to databases, etc. All these issues turn data mining into a highly

interdisciplinary subject involving tasks not only of typical data analysts but

also of people working with databases, data visualization on high dimensions,

etc.

R has limitations with handling enormous datasets because all computation

is carried out in the main memory of the computer. This does not mean that

we will not be able to handle these problems. Taking advantage of the highly

Introduction 3

R commands are entered at R command prompt, “>”. Whenever you see

this prompt you can interpret it as R waiting for you to enter a command.

You type in the commands at the prompt and then press the enter key to

ask R to execute them. This may or may not produce some form of output

(the result of the command) and then a new prompt appears. At the prompt

you may use the arrow keys to browse and edit previously entered commands.

This is handy when you want to type commands similar to what you have

done before as you avoid typing them again.

Still, you can take advantage of the code provided at the book Web site to

cut and paste between your browser or editor and the R console, thus avoiding

having to type all commands described in the book. This will surely facilitate

your learning experience and improve your understanding of its potential.

1.2 A Short Introduction to R

The goal of this section is to provide a brief introduction to the key issues of the

R language. We do not assume any familiarity with computer programming.

Readers should be able to easily follow the examples presented in this section.

Still, if you feel some lack of motivation to continue reading this introductory

material, do not worry. You may proceed to the case studies and then return

to this introduction as you get more motivated by the concrete applications.

R is a functional language for statistical computation and graphics. It

can be seen as a dialect of the S language (developed at AT&T) for which

John Chambers was awarded the 1998 Association for Computing Machinery

(ACM) Software award that mentioned that this language “forever altered

how people analyze, visualize and manipulate data”.

R can be quite useful just by using it in an interactive fashion at its com-

mand line. Still, more advanced uses of the system will lead the user to develop

his own functions to systematize repetitive tasks, or even to add or change

some functionalities of the existing add-on packages, taking advantage of being

open source.

1.2.1 Starting with R

In order to install R in your system, the easiest way is to obtain a bi-

nary distribution from the R Web site

where you can follow the link that

takes you to the CRAN (Comprehensive R Archive Network) site to obtain,

among other things, the binary distribution for your particular operating sys-

tem/architecture. If you prefer to build R directly from the sources, you can

get instructions on how to do it from CRAN.

http://www.R-project.org.

4 Data Mining with R: Learning with Case Studies

After downloading the binary distribution for your operating system you

just need to follow the instructions that come with it. In the case of the Win-

dows version, you simply execute the downloaded ﬁle (R-2.10.1-win32.exe)

and select the options you want in the following menus. In some operating

systems you may need to contact your system administrator to fulﬁll the in-

stallation task due to lack of permissions to install software.

To run R in Windows you simply double-click the appropriate icon on your

desktop, while in Unix versions you should type R at the operating system

prompt. Both will bring up the R console with its prompt “>”.

If you want to quit R you can issue the command q() at the prompt. You

will be asked if you want to save the current workspace. You should answer yes

only if you want to resume your current analysis at the point you are leaving

it, later on.

Although the set of tools that comes with R is by itself quite powerful,

it is natural that you will end up wanting to install some of the large (and

growing) set of add-on packages available for R at CRAN. In the Windows

version this is easily done through the “Packages” menu. After connecting

your computer to the Internet you should select the “Install package from

CRAN...” option from this menu. This option will present a list of the packages

available at CRAN. You select the one(s) you want, and R will download the

package(s) and self-install it(them) on your system. In Unix versions, things

may be slightly diﬀerent depending on the graphical capabilities of your R

installation. Still, even without selection from menus, the operation is simple.

Suppose you want to download the package that provides functions to connect

to MySQL databases. This package name is RMySQL.

Youjustneedtotype

the following command at R prompt:

> install.packages('RMySQL')

The install.packages() function has many parameters, among which

there is the repos argument that allows you to indicate the nearest CRAN

mirror.

Still, the ﬁrst time you run the function in an R session, it will prompt

you for the repository you wish to use.

One thing that you surely should do is install the package associated with

this book, which will give you access to several functions used throughout the

book as well as datasets. To install it you proceed as with any other package:

> install.packages('DMwR')

The actual name of the ﬁle changes with newer versions. This is the name for version

2.10.1.

Please note that the following code also works in Windows versions, although you may

ﬁnd the use of the menu more practical.

You can get an idea of the functionalities of each of the R packages in the R FAQ

(frequently asked questions) at CRAN.

The list of available mirrors can be found at http://cran.r-project.org/mirrors.

html.

Introduction 5

If you want to know the packages currently installed in your computer,

you can issue

> installed.packages()

This produces a long output with each line containing a package, its ver-

sion information, the packages it depends, and so on. A more user-friendly,

although less complete, list of the installed packages can be obtained by issuing

> library()

The following command can be very useful as it allows you to check whether

there are newer versions of your installed packages at CRAN:

> old.packages()

Moreover, you can use the following command to update all your installed

packages:

> update.packages()

R has an integrated help system that you can use to know more about the

system and its functionalities. Moreover, you can ﬁnd extra documentation

at the R site. R comes with a set of HTML ﬁles that can be read using a

Web browser. On Windows versions of R, these pages are accessible through

the help menu. Alternatively, you can issue help.start() at the prompt

to launch a browser showing the HTML help pages. Another form of getting

help is to use the help() function. For instance, if you want some help on the

plot() function, you can enter the command “help(plot)” (or alternatively,

?plot). A quite powerful alternative, provided you are connected to the In-

ternet, is to use the RSiteSearch() function that searches for key words or

phrases in the mailing list archives, R manuals, and help pages; for example,

> RSiteSearch('neural networks')

Finally, there are several places on the Web that provide help on several

facets of R, such as the site http://www.rseek.org/.

1.2.2 R Objects

There are two main concepts behind the R language: objects and functions.

An object can be seen as a storage space with an associated name. Everything

in R is stored in an object. All variables, data, functions, etc. are stored in the

memory of the computer in the form of named objects.

Functions are a special type of R objects designed to carry out some op-

eration. They usually take some arguments and produce a result by means of

executing some set of operations (themselves usually other function calls). R

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