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R Data Mining

You're reading from R Data Mining

Product type Book

Published in Nov 2017

Publisher Packt

ISBN-13 9781787124462

Pages 442 pages

Edition 1st Edition

Languages

Concepts

Table of Contents (22) Chapters

Title Page

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. Why to Choose R for Your Data Mining and Where to Start

2. A First Primer on Data Mining Analysing Your Bank Account Data

3. The Data Mining Process - CRISP-DM Methodology

4. Keeping the House Clean – The Data Mining Architecture

5. How to Address a Data Mining Problem – Data Cleaning and Validation

6. Looking into Your Data Eyes – Exploratory Data Analysis

7. Our First Guess – a Linear Regression

8. A Gentle Introduction to Model Performance Evaluation

9. Don't Give up – Power up Your Regression Including Multiple Variables

10. A Different Outlook to Problems with Classification Models

11. The Final Clash – Random Forests and Ensemble Learning

12. Looking for the Culprit – Text Data Mining with R

13. Sharing Your Stories with Your Stakeholders through R Markdown

14. Epilogue

15. Dealing with Dates, Relative Paths and Functions

User interface

Until now, we have been looking at the back office of our data mining architecture, which is the part not directly visible to its end user. Imagine this architecture is provided to be employed by someone not skilled enough to work on the data mining engine itself; we will need some way to let this user interact with the architecture in the right way, and discover the results of its interaction. This is what a user interface is all about.

In times when data mining is getting more and more popular every day, a big question is how to make it accessible to a great portion of professionals not classifiable as programmers and statisticians. This is where the user interfaces play a big role.

Even if user interface design is beyond the scope of this book, let's discover some well-established principles of good user interface design, so that you can apply them to evaluate alternative user interfaces and products you could come across on your data mining journey.

Clarity

This principle...

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