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You're reading from Learning Predictive Analytics with R

Product type Book

Published in Sep 2015

Publisher Packt

ISBN-13 9781782169352

Pages 332 pages

Edition 1st Edition

Languages

Concepts

Predictive Analytics

Author (1):

Eric Mayor

Table of Contents (23) Chapters

Learning Predictive Analytics with R

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

Setting GNU R for Predictive Analytics

Visualizing and Manipulating Data Using R

Data Visualization with Lattice

Cluster Analysis

Agglomerative Clustering Using hclust()

Dimensionality Reduction with Principal Component Analysis

Exploring Association Rules with Apriori

Probability Distributions, Covariance, and Correlation

Linear Regression

Classification with k-Nearest Neighbors and Naïve Bayes

Classification Trees

Multilevel Analyses

Text Analytics with R

Cross-validation and Bootstrapping Using Caret and Exporting Predictive Models Using PMML

Exercises and Solutions

Chapter 7. Exploring Association Rules with Apriori

Association rules allow us to explore the relationship between items and sets of items. Such items can be as diverse as the contents of a market basket, the words used in sentences, the components of food products, and so on. Let's go back to the first example: transactions in a shop. Each transaction is composed of one or more items. We are interested in transactions of at least two items because, of course, there cannot be relationships between several items in the purchase of a single item. Imagine customers are purchasing the following sets of items, for which each row represents a transaction. We will use this example more thoroughly in this section:

Cherry coke, chips, lemon
Cherry coke, chicken wings, lemon
Cherry coke, chips, chicken wings, lemon
Chips, chicken wings, lemon
Cherry coke, lemon, chips, chocolate cake

At first sight, you will notice that there seems to be an association between purchases of cherry coke and lemon, as four...

Apriori – basic concepts

There are some concepts about apriori that need to be understood before going further in this chapter: association rules, itemsets, support, confidence, and lift.

Association rules

An association rule is the explicit mention of a relationship in the data, in the form X => Y, where X (the antecedent) can be composed of one or several items. X is called an itemset. In what we will see, Y (the consequent) is always one single item. We might, for instance, be interested in what the antecedents of lemon are if we are interested in promoting the purchase of lemons.

Itemsets

Frequent itemsets are items or collections of items that occur frequently in transactions. Lemon is the most frequent itemset in the previous example, followed by cherry coke and chips. Itemsets are considered frequent if they occur more frequently than a specified threshold. This threshold is called minimal support. The omission of itemsets with support less than the minimal support is called support...

The inner working of apriori

The goal of apriori is to compute the frequent itemsets and the association rules in an efficient way, as well as to compute support and confidence for these. Going into the details of these computations is beyond the scope of this chapter. In what follows, we briefly examine how itemset generation and rule generation are accomplished.

Generating itemsets with support-based pruning

The most straightforward way to compute frequent itemsets would be to consider all the possible itemsets and discard those with support lower than minimal support. This is particularly inefficient, as generating itemsets and then discarding them is a waste of computation power. The goal is, of course, to generate only the itemsets that are useful for the analysis: those with support higher than minimal support. Let's continue with our previous example. The following table presents the same data using a binary representation:

Analyzing data with apriori in R

In this section, we will continue with another supermarket example and analyze associations in the Groceries dataset. In order to use this dataset and to explore association rules in R, we need to install and load the arules package:

install.packages("arules")
library(arules)
data(Groceries)

Using apriori for basic analysis

We can now explore relationships between purchased products in this dataset. This dataset is already in a form exploitable by apriori (transactions). We will first use the default parameters as follows:

rules = apriori(Groceries)

The output is provided in the following screenshot:

Running apriori on the Groceries dataset with default parameters

We can see on the first line the parameters used in the analysis—in this case, the default. Around the middle of the output (where the arrow is), we see that there are 169 items in 9835 transactions in this dataset, and that 0 rules have been found (see second to last line). If you try this with your own...

Summary

In this chapter, we discovered some important concepts regarding association rules. In particular, we examined how important support, confidence, and lift measures are in the assessment of association rules, and that high support and confidence do not necessarily mean that an association rule is useful. We uncovered the efficient working of the apriori algorithm for mining association rules and discovered the use of apriori in R in mining several datasets. We have also seen that it is often necessary to recode some variables before being able to analyze the data. Finally, we have discovered Grouped matrix-based visualization.

In next chapter, we will examine statistical distributions and correlations.

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Learning Predictive Analytics with R

Published in: Sep 2015 Publisher: Packt ISBN-13: 9781782169352

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Authors (1)

Eric Mayor

Eric Mayor is a senior researcher and lecturer at the University of Neuchatel, Switzerland. He is an enthusiastic user of open source and proprietary predictive analytics software packages, such as R, Rapidminer, and Weka. He analyzes data on a daily basis and is keen to share his knowledge in a simple way.

See other products by Eric Mayor