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R Data Science Essentials

You're reading from  R Data Science Essentials

Product type Book
Published in Jan 2016
Publisher
ISBN-13 9781785286544
Pages 154 pages
Edition 1st Edition
Languages

Table of Contents (15) Chapters

R Data Science Essentials
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with R 2. Exploratory Data Analysis 3. Pattern Discovery 4. Segmentation Using Clustering 5. Developing Regression Models 6. Time Series Forecasting 7. Recommendation Engine 8. Communicating Data Analysis Index

Apriori analysis


In order to perform Apriori analysis, we need to load the arules package. If the package has not been installed, use the install.packages function.

We can then apply the Apriori algorithm on the transactional data. In the previous section we created two different transactional datasets. Let's apply the Apriori algorithm on this dataset:

rules1<- apriori(Adult,parameter = list(sup = 0.5, conf = 0.9,target="rules"));

The output is as follows:

From the preceding output, we can see that there are 52 rules in total that are generated. In the preceding function, we use a few additional sup and conf parameters, which are nothing but support and confidence, respectively. We will explore these parameters in detail but, for now, we have the rules generated based on the inputs. We can then inspect the rules generated using the inspect function as follows:

inspect(rules1)

The following is the output:

The output is continued here:

Note

Note that this is just a random snapshot from the...

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