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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

Databases and data warehouses

It is now time to talk about the data warehouse and databases. We will have a look at their theoretical structure and some practical technology available on the market to build these kinds of instruments:

What is a data warehouse, and how is it different from a simple database?

A data warehouse is a software solution aimed at storing usually great amounts of data properly related among them and indexed through a time-related index. We can better understand this by looking at the data warehouse's cousin: the operational database.

These kinds of instruments are usually of small dimensions, and aimed at storing and inquiring data, overwriting old data when new data is available. Data warehouses are therefore usually fed by databases, and stores data from those kinds of sources ensuring a historical depth to them and read-only access from other users and software applications. Moreover, data warehouses are usually employed at a company level, to store, and make available...