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

Data sources


Data sources are everywhere. As the following picture tries to suggest, we can find data within all the realms of reality. This hyperbolic sentence is becoming more true thanks to the well-known trend of the internet of things, and now that every kind of object is getting connected to the internet, we are starting to collect data from tons of new physical sources. This data can come in a form already feasible for being collected and stored within our databases, or in a form that needs to be further modified to become usable for our analyses:

We can, therefore, see that between our data sources and the physical data warehouse where they are going to be stored, a small components lies, which is the set of tools and software needed to make data coming from sources storable.

We should note something here—we are not talking about data cleaning and data validation. Those activities will be performed later on by our data mining engine retrieving the data from our data warehouse. For...