Book Image

R Data Mining

Book Image

R Data Mining

Overview of this book

R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R. It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data. Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets.
Table of Contents (22 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
14
Epilogue

How to build a data mining architecture in R


Until now, we have been treating the data mining architecture topic at a general level, defining its components and their role within the system; but how do we build such kinds of architecture in R? This is what we are going to discover here. At the end of the paragraph, you will then be able not just to understand how a data mining architecture is composed but even how to build one for your own purposes.

To be clear, we have to specify from the beginning here that we are not going to build a firm-wide data mining architecture, but rather a small architecture like the ones needed to develop your first data mining projects with R. Once this is set, we can proceed with looking at each of the aforementioned components and how to implement them with our beloved  R language.

Data sources

As seen earlier, this is where everything begins: the data. R is well-known for being able to treat different kinds of data coming from a great variety of data sources...