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You're reading from R Data Mining Implement data mining techniques through practical use cases and real-world datasets

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781787124462

Length 442 pages

Edition 1st Edition

Languages

Tools

ggplot

Concepts

Data Mining

Author (1):

Andrea Cirillo

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Table of Contents (16) Chapters

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 FREE CHAPTER

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

A general overview

Let's first gain a general view on the main components of a data mining architecture. It is basically composed of all of the basic elements you will need to perform the activities described in the previous chapter. As a minimum set of components, the following are usually considered:

Data sources
Data warehouse
Data mining engine
User interface

We can find reproduced here a useful logical map of a data mining architecture, where each of the mentioned components is depicted, highlighting through grey arrows the one and two verses connection within the components:

We are going to get into more details in the next paragraph; nevertheless, we can briefly have a look at all these components to get a clear sense of their reciprocal relationships:

Data sources: These are all the possible sources of small bits of information to be analyzed. Data sources feed our data warehouses and are fed by the data produced from our activity toward the user interface.
Data warehouse: This is where...