Data Mining for the Masses



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Data Mining  
for the Masses 
 
 
 
Dr. Matthew North


 
ii 
 
 
 
 
A Global Text Project Book 
This book is available on Amazon.com. 
© 2012 Dr. Matthew A. North 
This book is licensed under a 
Creative Commons Attribution 3.0 License
 
All rights reserved. 
ISBN:
 
0615684378 
ISBN-13: 978-0615684376 
 


 
iii 
 
DEDICATION 
 
 
This book is gratefully dedicated to Dr. Charles Hannon, who gave me the chance to become a 
college professor and then challenged me to learn how to teach data mining to the masses.


 
iv 
 
 
 


Data Mining for the Masses 

 
Table of Contents 
 
Dedication ....................................................................................................................................................... iii 
Table of Contents ............................................................................................................................................ v 
Acknowledgements ........................................................................................................................................ xi 
SECTION ONE: Data Mining Basics ......................................................................................................... 1 
Chapter One: Introduction to Data Mining and CRISP-DM .................................................................. 3 
Introduction ................................................................................................................................................. 3 
A Note About Tools .................................................................................................................................. 4 
The Data Mining Process .......................................................................................................................... 5 
Data Mining and You ...............................................................................................................................11 
Chapter Two: Organizational Understanding and Data Understanding ..............................................13 
Context and Perspective ..........................................................................................................................13 
Learning Objectives ..................................................................................................................................14 
Purposes, Intents and Limitations of Data Mining ..............................................................................15 
Database, Data Warehouse, Data Mart, Data Set…? ..........................................................................15 
Types of Data ............................................................................................................................................19 
A Note about Privacy and Security ........................................................................................................20 
Chapter Summary......................................................................................................................................21 
Review Questions......................................................................................................................................22 
Exercises .....................................................................................................................................................22 
Chapter Three: Data Preparation ................................................................................................................25 
Context and Perspective ..........................................................................................................................25 
Learning Objectives ..................................................................................................................................25 
Collation .....................................................................................................................................................27 


Data Mining for the Masses 
vi 
Data Scrubbing ......................................................................................................................................... 28 
Hands on Exercise .................................................................................................................................... 29 
Preparing RapidMiner, Importing Data, and ........................................................................................ 30 
Handling Missing Data ............................................................................................................................ 30 
Data Reduction ......................................................................................................................................... 46 
Handling Inconsistent Data .................................................................................................................... 50 
Attribute Reduction .................................................................................................................................. 52 
Chapter Summary ..................................................................................................................................... 54 
Review Questions ..................................................................................................................................... 55 
Exercise ...................................................................................................................................................... 55 
SECTION TWO: Data Mining Models and Methods ........................................................................... 57 
Chapter Four: Correlation ........................................................................................................................... 59 
Context and Perspective .......................................................................................................................... 59 
Learning Objectives.................................................................................................................................. 59 
Organizational Understanding ................................................................................................................ 59 
Data Understanding ................................................................................................................................. 60 
Data Preparation ....................................................................................................................................... 60 
Modeling .................................................................................................................................................... 62 
Evaluation .................................................................................................................................................. 63 
Deployment ............................................................................................................................................... 65 
Chapter Summary ..................................................................................................................................... 67 
Review Questions ..................................................................................................................................... 68 
Exercise ...................................................................................................................................................... 68 
Chapter Five: Association Rules ................................................................................................................. 73 
Context and Perspective .......................................................................................................................... 73 
Learning Objectives.................................................................................................................................. 73 
Organizational Understanding ................................................................................................................ 73 




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