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Machine Learning with R

You're reading from   Machine Learning with R R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required ‚Äì this book will take you methodically through every stage of applying machine learning.

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Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781782162148
Length 396 pages
Edition 1st Edition
Languages
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Author (1):
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Brett Lantz Brett Lantz
Author Profile Icon Brett Lantz
Brett Lantz
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Table of Contents (19) Chapters Close

Machine Learning with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Introducing Machine Learning 2. Managing and Understanding Data FREE CHAPTER 3. Lazy Learning – Classification Using Nearest Neighbors 4. Probabilistic Learning – Classification Using Naive Bayes 5. Divide and Conquer – Classification Using Decision Trees and Rules 6. Forecasting Numeric Data – Regression Methods 7. Black Box Methods – Neural Networks and Support Vector Machines 8. Finding Patterns – Market Basket Analysis Using Association Rules 9. Finding Groups of Data – Clustering with k-means 10. Evaluating Model Performance 11. Improving Model Performance 12. Specialized Machine Learning Topics Index

Understanding association rules


The result of a market basket analysis is a set of association rules that specify patterns of relationships among items. A typical rule might be expressed in the form:

In plain language, this association rule states that if peanut butter and jelly are purchased, then bread is also likely to be purchased. In other words, "peanut butter and jelly imply bread." Groups of one or more items are surrounded by brackets to indicate that they form a set, or more specifically, an itemset that appears in the data with some regularity. Association rules are learned from subsets of itemsets. For example, the preceding rule was identified from the set of {peanut butter, jelly, bread}.

Developed in the context of Big Data and database science, association rules are not used for prediction, but rather for unsupervised knowledge discovery in large databases, unlike the classification and numeric prediction algorithms presented in previous chapters. Even so, you will find that...

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