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

Summary


By the end of this chapter, you may feel like the adrenaline accumulated within the first two has got a bit diluted, but you would be wrong. This chapter was all about discovering the basic ingredients of a well-conceived and well-conducted data mining project, namely:

  • The data sources (where to find data to answer your questions)
  • The data warehouse (where to properly store your data to have it at your disposal when needed)
  • The data mining engine (to perform your data modelling activities, and get knowledge from your data)
  • The user interface (to interact with your engine)

We are now ready to leave the quiet village where our apprentice took place, where we discovered our weapon, its power and the way to use it. A real and complex problem is waiting for us just on the next page. Good luck, my dear reader.