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Text Content

 * Resume
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SALEM MARAFI




MARKET BASKET ANALYSIS WITH R

Posted by Salem on March 19, 2014

116 Comments





Source: Paul’s Health Blog


ASSOCIATION RULES

There are many ways to see the similarities between items. These are techniques
that fall under the general umbrella of association. The outcome of this type of
technique, in simple terms, is a set of rules that can be understood as “if
this, then that”.


APPLICATIONS

So what kind of items are we talking about?
There are many applications of association:

 * Product recommendation – like Amazon’s “customers who bought that, also
   bought this”
 * Music recommendations – like Last FM’s artist recommendations
 * Medical diagnosis – like with diabetes really cool stuff
 * Content optimisation – like in magazine websites or blogs

In this post we will focus on the retail application – it is simple, intuitive,
and the dataset comes packaged with R making it repeatable.


THE GROCERIES DATASET

Imagine 10000 receipts sitting on your table. Each receipt represents a
transaction with items that were purchased. The receipt is a representation of
stuff that went into a customer’s basket – and therefore ‘Market Basket
Analysis’.

That is exactly what the Groceries Data Set contains: a collection of receipts
with each line representing 1 receipt and the items purchased. Each line is
called a transaction and each column in a row represents an item. You can
download the Groceries data set to take a look at it, but this is not a
necessary step.


A LITTLE BIT OF MATH

We already discussed the concept of Items and Item Sets.

We can represent our items as an item set as follows:

I = { i1,i2,…,in }

Therefore a transaction is represented as follows:

tn = { ij,ik,…,in }

This gives us our rules which are represented as follows:

{ i1,i2} => { ik}

Which can be read as “if a user buys an item in the item set on the left hand
side, then the user will likely buy the item on the right hand side too”. A more
human readable example is:

{coffee,sugar} => {milk}

If a customer buys coffee and sugar, then they are also likely to buy milk.

With this we can understand three important ratios; the support, confidence and
lift. We describe the significance of these in the following bullet points, but
if you are interested in a formal mathematical definition you can find it on
wikipedia.

 * Support: The fraction of which our item set occurs in our dataset.
 * Confidence: probability that a rule is correct for a new transaction with
   items on the left.
 * Lift: The ratio by which by the confidence of a rule exceeds the expected
   confidence.
   Note: if the lift is 1 it indicates that the items on the left and right are
   independent.


   APRIORI RECOMMENDATION WITH R

   So lets get started by loading up our libraries and data set.

   # Load the libraries
   library(arules)
   library(arulesViz)
   library(datasets)
    
   # Load the data set
   data(Groceries)
   
   # Load the libraries library(arules) library(arulesViz) library(datasets) #
   Load the data set data(Groceries)

   Lets explore the data before we make any rules:

   # Create an item frequency plot for the top 20 items
   itemFrequencyPlot(Groceries,topN=20,type="absolute")
   
   # Create an item frequency plot for the top 20 items
   itemFrequencyPlot(Groceries,topN=20,type="absolute")

   

   We are now ready to mine some rules!
   You will always have to pass the minimum required support and confidence.

    * We set the minimum support to 0.001
    * We set the minimum confidence of 0.8
    * We then show the top 5 rules

   # Get the rules
   rules <- apriori(Groceries, parameter = list(supp = 0.001, conf = 0.8))
    
   # Show the top 5 rules, but only 2 digits
   options(digits=2)
   inspect(rules[1:5])
   
   # Get the rules rules <- apriori(Groceries, parameter = list(supp = 0.001,
   conf = 0.8)) # Show the top 5 rules, but only 2 digits options(digits=2)
   inspect(rules[1:5])

   The output we see should look something like this

     lhs                        rhs             support      confidence  lift
   1 {liquor,red/blush wine} => {bottled beer}  0.0019       0.90        11.2
   2 {curd,cereals}          => {whole milk}    0.0010       0.91        3.6
   3 {yogurt,cereals}        => {whole milk}    0.0017       0.81        3.2
   4 {butter,jam}            => {whole milk}    0.0010       0.83        3.3
   5 {soups,bottled beer}    => {whole milk}    0.0011       0.92        3.6
   
   lhs rhs support confidence lift 1 {liquor,red/blush wine} => {bottled beer}
   0.0019 0.90 11.2 2 {curd,cereals} => {whole milk} 0.0010 0.91 3.6 3
   {yogurt,cereals} => {whole milk} 0.0017 0.81 3.2 4 {butter,jam} => {whole
   milk} 0.0010 0.83 3.3 5 {soups,bottled beer} => {whole milk} 0.0011 0.92 3.6

   This reads easily, for example: if someone buys yogurt and cereals, they are
   81% likely to buy whole milk too.

   We can get summary info. about the rules that give us some interesting
   information such as:

    * The number of rules generated: 410
    * The distribution of rules by length: Most rules are 4 items long
    * The summary of quality measures: interesting to see ranges of support,
      lift, and confidence.
    * The information on the data mined: total data mined, and minimum
      parameters.

   set of 410 rules
    
   rule length distribution (lhs + rhs): sizes
     3   4   5   6 
    29 229 140  12 
    
   summary of quality measures:
            support     conf.           lift     
    Min.   :0.00102     Min.   :0.80    Min.   : 3.1  
    1st Qu.:0.00102     1st Qu.:0.83    1st Qu.: 3.3  
    Median :0.00122     Median :0.85    Median : 3.6  
    Mean   :0.00125     Mean   :0.87    Mean   : 4.0  
    3rd Qu.:0.00132     3rd Qu.:0.91    3rd Qu.: 4.3  
    Max.   :0.00315     Max.   :1.00    Max.   :11.2  
    
   mining info:
         data      n      support   confidence
    Groceries      9835   0.001     0.8
   
   set of 410 rules rule length distribution (lhs + rhs): sizes 3 4 5 6 29 229
   140 12 summary of quality measures: support conf. lift Min. :0.00102 Min.
   :0.80 Min. : 3.1 1st Qu.:0.00102 1st Qu.:0.83 1st Qu.: 3.3 Median :0.00122
   Median :0.85 Median : 3.6 Mean :0.00125 Mean :0.87 Mean : 4.0 3rd Qu.:0.00132
   3rd Qu.:0.91 3rd Qu.: 4.3 Max. :0.00315 Max. :1.00 Max. :11.2 mining info:
   data n support confidence Groceries 9835 0.001 0.8


   SORTING STUFF OUT

   The first issue we see here is that the rules are not sorted. Often we will
   want the most relevant rules first. Lets say we wanted to have the most
   likely rules. We can easily sort by confidence by executing the following
   code.

   rules<-sort(rules, by="confidence", decreasing=TRUE)
   
   rules<-sort(rules, by="confidence", decreasing=TRUE)

   Now our top 5 output will be sorted by confidence and therefore the most
   relevant rules appear.

     lhs                                            rhs            support  conf.  lift
   1 {rice,sugar}                                => {whole milk}   0.0012   1      3.9
   2 {canned fish,hygiene articles}              => {whole milk}   0.0011   1      3.9
   3 {root vegetables,butter,rice}               => {whole milk}   0.0010   1      3.9
   4 {root vegetables,whipped/sour cream,flour}  => {whole milk}   0.0017   1      3.9
   5 {butter,soft cheese,domestic eggs}          => {whole milk}   0.0010   1      3.9
   
   lhs rhs support conf. lift 1 {rice,sugar} => {whole milk} 0.0012 1 3.9 2
   {canned fish,hygiene articles} => {whole milk} 0.0011 1 3.9 3 {root
   vegetables,butter,rice} => {whole milk} 0.0010 1 3.9 4 {root
   vegetables,whipped/sour cream,flour} => {whole milk} 0.0017 1 3.9 5
   {butter,soft cheese,domestic eggs} => {whole milk} 0.0010 1 3.9

   Rule 4 is perhaps excessively long. Lets say you wanted more concise rules.
   That is also easy to do by adding a “maxlen” parameter to your apriori
   function:

   rules <- apriori(Groceries, parameter = list(supp = 0.001, conf = 0.8,maxlen=3))
   
   rules <- apriori(Groceries, parameter = list(supp = 0.001, conf =
   0.8,maxlen=3))


   REDUNDANCIES

   Sometimes, rules will repeat. Redundancy indicates that one item might be a
   given. As an analyst you can elect to drop the item from the dataset.
   Alternatively, you can remove redundant rules generated.

   We can eliminate these repeated rules using the follow snippet of code:

   subset.matrix <- is.subset(rules, rules)
   subset.matrix[lower.tri(subset.matrix, diag=T)] <- NA
   redundant <- colSums(subset.matrix, na.rm=T) >= 1
   rules.pruned <- rules[!redundant]
   rules<-rules.pruned
   
   subset.matrix <- is.subset(rules, rules)
   subset.matrix[lower.tri(subset.matrix, diag=T)] <- NA redundant <-
   colSums(subset.matrix, na.rm=T) >= 1 rules.pruned <- rules[!redundant]
   rules<-rules.pruned


   TARGETING ITEMS

   Now that we know how to generate rules, limit the output, lets say we wanted
   to target items to generate rules. There are two types of targets we might be
   interested in that are illustrated with an example of “whole milk”:

    1. What are customers likely to buy before buying whole milk
    2. What are customers likely to buy if they purchase whole milk?

   This essentially means we want to set either the Left Hand Side and Right
   Hand Side. This is not difficult to do with R!

   Answering the first question we adjust our apriori() function as follows:

   rules<-apriori(data=Groceries, parameter=list(supp=0.001,conf = 0.08), 
                  appearance = list(default="lhs",rhs="whole milk"),
                  control = list(verbose=F))
   rules<-sort(rules, decreasing=TRUE,by="confidence")
   inspect(rules[1:5])
   
   rules<-apriori(data=Groceries, parameter=list(supp=0.001,conf = 0.08),
   appearance = list(default="lhs",rhs="whole milk"), control = list(verbose=F))
   rules<-sort(rules, decreasing=TRUE,by="confidence") inspect(rules[1:5])

   The output will look like this:

     lhs                                              rhs          supp.   conf.  lift
   1 {rice,sugar}                                 => {whole milk}  0.0012   1     3.9
   2 {canned fish,hygiene articles}               => {whole milk}  0.0011   1     3.9
   3 {root vegetables,butter,rice}                => {whole milk}  0.0010   1     3.9
   4 {root vegetables,whipped/sour cream,flour}   => {whole milk}  0.0017   1     3.9
   5 {butter,soft cheese, domestic eggs}          => {whole milk}  0.0010   1     3.9
   
   lhs rhs supp. conf. lift 1 {rice,sugar} => {whole milk} 0.0012 1 3.9 2
   {canned fish,hygiene articles} => {whole milk} 0.0011 1 3.9 3 {root
   vegetables,butter,rice} => {whole milk} 0.0010 1 3.9 4 {root
   vegetables,whipped/sour cream,flour} => {whole milk} 0.0017 1 3.9 5
   {butter,soft cheese, domestic eggs} => {whole milk} 0.0010 1 3.9

   Likewise, we can set the left hand side to be “whole milk” and find its
   antecedents.
   Note the following:

    * We set the confidence to 0.15 since we get no rules with 0.8
    * We set a minimum length of 2 to avoid empty left hand side items

   rules<-apriori(data=Groceries, parameter=list(supp=0.001,conf = 0.15,minlen=2), 
                  appearance = list(default="rhs",lhs="whole milk"),
                  control = list(verbose=F))
   rules<-sort(rules, decreasing=TRUE,by="confidence")
   inspect(rules[1:5])
   
   rules<-apriori(data=Groceries, parameter=list(supp=0.001,conf =
   0.15,minlen=2), appearance = list(default="rhs",lhs="whole milk"), control =
   list(verbose=F)) rules<-sort(rules, decreasing=TRUE,by="confidence")
   inspect(rules[1:5])

   Now our output looks like this:

     lhs             rhs                support confidence lift
   1 {whole milk} => {other vegetables}   0.075       0.29  1.5
   2 {whole milk} => {rolls/buns}         0.057       0.22  1.2
   3 {whole milk} => {yogurt}             0.056       0.22  1.6
   4 {whole milk} => {root vegetables}    0.049       0.19  1.8
   5 {whole milk} => {tropical fruit}     0.042       0.17  1.6
   6 {whole milk} => {soda}               0.040       0.16  0.9
   
   lhs rhs support confidence lift 1 {whole milk} => {other vegetables} 0.075
   0.29 1.5 2 {whole milk} => {rolls/buns} 0.057 0.22 1.2 3 {whole milk} =>
   {yogurt} 0.056 0.22 1.6 4 {whole milk} => {root vegetables} 0.049 0.19 1.8 5
   {whole milk} => {tropical fruit} 0.042 0.17 1.6 6 {whole milk} => {soda}
   0.040 0.16 0.9


   VISUALIZATION

   The last step is visualization. Lets say you wanted to map out the rules in a
   graph. We can do that with another library called “arulesViz”.

   library(arulesViz)
   plot(rules,method="graph",interactive=TRUE,shading=NA)
   
   library(arulesViz) plot(rules,method="graph",interactive=TRUE,shading=NA)

   You will get a nice graph that you can move around to look like this:

   


   REFERENCES

    * Snowplow Market Basket Analysis
    * Discovering Knowledge in Data: An Introduction to Data Mining
    * RDatamining.com


   RESOURCES

    * Groceries Dataset

   Merken
   Tweet
   
   
   
   

Category: Code

Tags: apriori, arules, arulesViz, association, data, data mining, mining, R


116 COMMENTS ON “MARKET BASKET ANALYSIS WITH R”


COMMENT NAVIGATION

← Older Comments

 1.  August 17, 2016  by  Arfin
     
     Hello,
     
     I have selected topic – Data mining in MBA using apriori algorithm, for my
     m.tech cse project. I am very new in data mining
     
     I am not able to understand which tools i need to use for this. And most
     important what actually i am suppose to do in it, i mean do i have to make
     an application for doing MBA using programing or something else.
     I know this can be a foolish question!!!!!!!!!
     But please guide me to understand about what should i do actually in this.
     
     Waiting for reply…..
     
     Reply
     
 2.  September 9, 2016  by  Shashank
     
     Hi Salem, great work !! Are you planning to do market basket analysis using
     python as well ? Keep up the good work.
     
     Reply
     
 3.  September 30, 2016  by  Carlos
     
     Hi, I’m working on a project and I found very useful your code! Thanks!
     But I have a question, how am I supposed to do this very analysis with my
     own data?
     When I read a .csv and transform to data frame, I’m in trouble since the
     itemFrequencyPlot command…
     
     Thanks again
     
     Carlos
     
     Reply
     
 4.  October 25, 2016  by  Bárbara Olave
     
     Thanks a lot!
     
     Reply
     
 5.  October 26, 2016  by  sreenix
     
     It seems that the raw data file is a package file and has been
     processed?using data(Groceries) followed by class(groceries) show as
     transactions attr (“package”). can anyone share how to start from data in a
     csv file and pre-process before using arules? thanks in advance
     
     Reply
     * January 17, 2017  by  Mike
       
       The arules documentation indicates that data in the function
       apriori(data, parameter = NULL, appearance = NULL, control = NULL), must
       be an object of class transactions.
       
       The documentation also includes information on the transactions class,
       and how to coerce objects of other classes (e.g. lists) into
       transactions.
       
       Reply
       
     * June 11, 2017  by  krupa kapadia
       
       I believe its need to be T/F or 0/1 Matrix. With each item as a column
       name and transactions as rows
       
       Reply
       
     
 6.  November 14, 2016  by  shantala
     
     Hi Salem,
     
     Thanks for the article. I was working on a similar dataset and your article
     was very informative.
     Very beautifully and neatly explained all the steps and concepts.
     
     Reply
     
 7.  November 16, 2016  by  Ged
     
     Nice and clear!
     
     Reply
     

 8.  Pingback: Affinity Analysis – Big Data Analytic by True

     
 9.  January 23, 2017  by  Riz
     
     Awesome explanation… Salem.
     
     Reply
     
 10. February 9, 2017  by  Jasper
     
     Thnx for the code. I found it very usefull for my project. Mainly the
     interactive graphs are great!
     
     Reply
     
 11. April 25, 2017  by  Rui
     
     Hi Salem, great work. Thank you for your explanation and code.
     Can you please give more details about the dataset (groceries.csv)? Where
     did you get it?
     
     Thanks
     Rui
     
     Reply
     
 12. May 25, 2017  by  Gopal Shah,Gujrat,India
     
     Sir,at present i am working on Association Rule.I am confusing that in
     given data say groceries, i want to find no. of association rules with
     Support and Confidence.It is possible in R language.Ex. say
     Suppprt confidence No. opf association Rule
     23 77.56 33
     … … ..
     pl. help me as early as possible.because i am handicap in this issue.
     Heartily request.
     
     Reply
     
 13. July 18, 2017  by  Tajdar Khan
     
     Very nice post thanks a lot.
     
     Reply
     
 14. July 19, 2017  by  saravana
     
     Hi, Thanks for article, I have a question that How to gather data or query
     data which is vry appropriate to apriori algorithm. It would be useful if
     anyone give me some advice and thought.
     
     Thanks.
     
     Reply
     
 15. July 21, 2017  by  Prat
     
     My R does not have groceries data set , could anyone upload it here?
     
     Reply
     * July 22, 2017  by  Salem
       
       Hi Prat,
       
       Sure, I added the attachment under resources. Good luck!
       
       Salem
       
       Reply
       
     
 16. August 23, 2017  by  Scott
     
     Salem – Thank you very much for this article! Your writing and code
     examples are very clear. This really helped me understand market basket
     analysis. What are your favorite applications of market basket analysis?
     Best – Scott
     
     Reply
     
 17. September 23, 2017  by  vishal
     
     Hi Salem,
     
     I have a doubt concerning the code output for checking redundancy. I got an
     output that said “set of 0 rules”, after executing : rules <- rules.pruned.
     Does it mean there are no redundancies in my dataset?
     
     Thanks in advance and oh, I love reading your blog!
     
     Reply
     
 18. September 26, 2017  by  Nandha
     
     After running the redundancy section of the code, I get the output which
     says that rules is empty.
     How do you fix this?
     
     Reply
     
 19. November 1, 2017  by  Tyler
     
     Would you possibly know why when I use the code above to remove redundant
     rules, it ends up saying all of my rules are redundant? This leads me to
     having an empty set of rules.
     
     Thanks!
     
     Reply
     
 20. November 9, 2017  by  Erick
     
     Did Arules change recently with R3.4? I have used it before, and previously
     I just took support and multiplied it by the total transactions count that
     I fed into the algorithm to get the number of transactions for that rule.
     I’m running this exercise on a large dataset (hundreds of thousands) and
     I’m not getting the total transaction count that I expected. Is it possible
     I’m not getting enough decimal precision back from arules?
     
     Thanks,
     
     Reply
     * November 9, 2017  by  Erick
       
       Also I am using as(rules_1, “data.frame”); to export that dataset to
       another file. I see now there is a options(digits=x) option. I might try
       that.
       
       Reply
       
     

 21. Pingback: Some Data Processing and Analysis with Python | sandipanweb

     
 22. January 31, 2018  by  sheeba
     
     pls share the groceries datasets with its unit price
     
     Reply
     
 23. February 2, 2018  by  raghavi
     
     library(arulesViz)
     Error: package or namespace load failed for ‘arulesViz’ in
     loadNamespace(j plot(rules,method=”graph”,interactive=TRUE,shading=NA)
     Error in plot.associations(rules, method = “graph”, interactive = TRUE, :
     Needed package ‘arulesViz’ not installed or loaded!
     
     can you resolve the above error
     
     Reply
     
 24. March 25, 2018  by  Harshit Singhal
     
     Nice Work
     
     Reply
     
 25. March 27, 2018  by  John
     
     I must performing association rules in R and i found the example
     here
     http://www.salemmarafi.com/code/market-basket-analysis-with-r/
     In this example they work with `data(Groceries)`
     original dataset Groceries.csv
     
     g=read.csv(“Groceries.csv”,sep=”;”)
     
     so i must convert it to transactions like arule requires
     
     #’@importClassesFrom arules transactions
     trans = as(g, “transactions”)
     
     lets’ examinate data(Groceries)
     
     > str(Groceries)
     Formal class ‘transactions’ [package “arules”] with 3 slots
     ..@ data :Formal class ‘ngCMatrix’ [package “Matrix”] with 5 slots
     .. .. ..@ i : int [1:43367] 13 60 69 78 14 29 98 24 15 29 …
     .. .. ..@ p : int [1:9836] 0 4 7 8 12 16 21 22 27 28 …
     .. .. ..@ Dim : int [1:2] 169 9835
     .. .. ..@ Dimnames:List of 2
     .. .. .. ..$ : NULL
     .. .. .. ..$ : NULL
     .. .. ..@ factors : list()
     ..@ itemInfo :’data.frame’: 169 obs. of 3 variables:
     .. ..$ labels: chr [1:169] “frankfurter” “sausage” “liver loaf” “ham” …
     .. ..$ level2: Factor w/ 55 levels “baby food”,”bags”,..: 44 44 44 44 44 44
     44 42 42 41 …
     .. ..$ level1: Factor w/ 10 levels “canned food”,..: 6 6 6 6 6 6 6 6 6 6 …
     ..@ itemsetInfo:’data.frame’: 0 obs. of 0 variables
     >
     
     and my converted data from original csv
     
     > str(trans)
     Formal class ‘transactions’ [package “arules”] with 3 slots
     ..@ data :Formal class ‘ngCMatrix’ [package “Matrix”] with 5 slots
     .. .. ..@ i : int [1:9835] 1265 6162 6377 4043 3585 6475 4431 3535 4401
     6490 …
     .. .. ..@ p : int [1:9836] 0 1 2 3 4 5 6 7 8 9 …
     .. .. ..@ Dim : int [1:2] 7011 9835
     .. .. ..@ Dimnames:List of 2
     .. .. .. ..$ : NULL
     .. .. .. ..$ : NULL
     .. .. ..@ factors : list()
     ..@ itemInfo :’data.frame’: 7011 obs. of 3 variables:
     .. ..$ labels : chr [1:7011] “tr=abrasive cleaner” “tr=abrasive
     cleaner,napkins” “tr=artif. sweetener” “tr=artif. sweetener,coffee” …
     .. ..$ variables: Factor w/ 1 level “tr”: 1 1 1 1 1 1 1 1 1 1 …
     .. ..$ levels : Factor w/ 7011 levels “abrasive cleaner”,..: 1 2 3 4 5 6 7
     8 9 10 …
     ..@ itemsetInfo:’data.frame’: 9835 obs. of 1 variable:
     .. ..$ transactionID: chr [1:9835] “1” “2” “3” “4” …
     >
     
     We see that in data(Groceries)
     
     transactions in sparse format with
     9835 transactions (rows) and
     169 items (columns)
     
     in my trans data
     
     9835 transactions (rows) and
     7011 items (columns)
     
     i.e. i got 7011 columns from Groceries.csv, meanwhile in embedded
     example(169 columns)
     
     Why it is so? How this file convert correct.
     I must understand it, cause, i can’t work with my file
     
     Reply
     
 26. April 24, 2018  by  Anatoly
     
     Hi, Salem! Awesome article!
     I’ve got one question about plots.
     When i’m trying to draw them, i’ve got some pink circles between arrows.
     Don’t you know what are they?
     
     Reply
     
 27. July 7, 2018  by  Dave
     
     On running the script
     itemFrequencyPlot(groceries, topN=20, type=“absolute”)
     
     I get an message
     Unable to find an inherited method for Function ‘itemFrequencyPlot’ for
     signature ‘data.frame’
     
     Help
     
     Reply
     
 28. August 10, 2018  by  SC
     
     Just wondering, how are left hand side and right hand side distinguished?
     Given the same transaction number, how do you know whether milk is bought
     first or last? Thanks.
     
     Reply
     
 29. September 23, 2018  by  R-ALGO
     
     Very cool tutorial the on grocery transactions.
     
     Reply
     

 30. Pingback: Curated list of R tutorials for Data Science - Meetkumar

     
 31. January 7, 2019  by  Prasath Panneerselvam
     
     Hi Salem,
     
     I am doing MBA and project in Market basket analyzes to find the itemset
     buying behavior of the customer.
     
     Could you please explain how to learn the program to write it as I am not
     software and I am a tech-support engineer. Is there any programming
     language is required.
     
     Thank you.
     
     Reply
     
 32. April 22, 2019  by  NBHA
     
     when I am uploading dataset to R it is showing me large transaction.. how
     can i convert that to transactional format in R?
     
     Reply
     


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