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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 FREE CHAPTER

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

The Crisp-DM methodology data mining cycle

The CRISP-DM methodology considers the analytical activities as a cyclical set of phases to be repeated until a satisfactory result is obtained. Not surprisingly then, Crisp-DM methodology phases are usually represented as a circle going from business understanding to the final deployment:

As we can see within the diagram, the cycle is composed of six phases:

Business understanding
Data understanding
Data preparation
Modeling
Evaluation
Deployment

This is the greater abstraction level of the Crisp-DM methodology, meaning one that can apply, with no exception, to all data mining problems. Three more specific layers are then conceived as a conjunction between the general model and the specific data mining project:

Generic tasks
Specialized tasks
Process instances

All of the components of every level are mapped to one component of the layer above, so that when dealing with a specific data mining problem, both bottom-up and top-down approaches are allowed, as...