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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

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...

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