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1、Data Mining with R:learning by case studiesLuis TorgoLIACC-FEP, University of PortoR. Campo Alegre, 823 - 4150 Porto, Portugal email: ltorgoliacc.up.pthttp:/www.liacc.up.pt/ltorgoMay 22, 2003PrefaceThe main goal of this book is to introduce the reader to the use of R as a tool for performing data mi
2、ning. R is a freely downloadable1 language and environment for statistical computing and graphics. Its capabilities and the large set of available packages make this tool an excellent alternative to the existing (andexpensive!) data mining tools.One of the key issues in data mining is size. A typica
3、l data mining problem involves a large database from where one seeks to extract useful knowledge. In this book we will use MySQL as the core database management system. MySQL is also freely available2 for several computer platforms. This means that you will be able to perform “serious” data mining w
4、ithout having to pay any money at all. Moreover, we hope to show you that this comes with no compromise in the quality of the obtained solutions. Expensive tools do not necessarily mean better tools! R together with MySQL form a pair very hard to beat as long as you are willing to spend some time le
5、arning how to use them. We think that it is worthwhile, and we hope that you are convinced as well at the end of reading this book.The goal of this book is not to describe all facets of data mining processes. Many books exist that cover this area. Instead we propose to introduce the reader to the po
6、wer of R and data mining by means of several case studies. Obviously, these case studies do not represent all possible data mining problems that one can face in the real world. Moreover, the solutions we describe can not be taken as complete solutions. Our goal is more to introduce the reader to the
7、 world of data mining using R through pratical examples. As such our analysis of the cases studies has the goal of showing examples of knowledge extraction using R, instead of presenting complete reports of data mining case studies. They should be taken as examples of possible paths in any data mini
8、ng project and can be used as the basis for developping solutions for the readers data mining projects. Still, we have tried to cover a diverse set of problems posing different challenges in terms of size, type of data, goals of analysis and tools that are necessary to carry out this analysis.We do
9、not assume any prior knowledge about R. Readers that are new to R and data mining should be able to follow the case studies. We have tried to make the different case studies self-contained in such a way that the reader can start anywhere in the document. Still, some basic R functionalities are intro
10、duced in the first, simpler, case studies, and are not repeated, which means that if you are new to R, then you should at least start with the first casevivstudies to get acquainted with R. Moreover, the first chapter provides a very short introduction to R basics, which may facilitate the understan
11、ding of the following chapters. We also do not assume any familiarity with data mining or statistical techniques. Brief introductions to different modeling approachesare provided as they are necessary in the case studies. It is not an objective of this book to provide the reader with full informatio
12、n on the technical and theoretical details of these techniques. Our descriptions of these models are given to provide basic understanding on their merits, drawbacks and analysis objectives. Other existing books should be considered if further theoretical insights are required. At the end of some sec
13、tions we provide “Further readings” pointers for the readers interested in knowing more on the topics. In summary, our target readers are more users of data analysis tools than researchers or developers. Still, we hope the latter also find reading this book useful as a form of entering the “world” o
14、f R and data mining.The book is accompanied by a set of freely available R source files that canbe obtained at the book Web site3. These files include all the code used in thecase studies. They facilitate the “do it yourself” philosophy followed in this document. We strongly recommend that readers i
15、nstall R and try the code as they read the book. All data used in the case studies is available at the book Web site as well.Contentsiii1. .2. .3. .3. .5. .6. .8. .9. .11. .12. .14. .17. .20. .23. .25. .28. .2933. .33. .34. .34. .35. .42. .43. .44.45Preface1 Introduction1.1How to read this book? . . . . . . . . . . . . . . . . . . . . .1.2A short introduction to R . . . . . . . . . . . . . . . . . . . .1.2.1Starting with R . . . . . . . . . . . . . . . . . . . . . .1.2.2R objects . . . . . .
