Togaware DATA MINING
Desktop Survival Guide
by Graham Williams
Google

Survey Data: Data Preparation

For this example we will use the survey dataset (see See Section 30.3.4). This dataset is a reasonable size and has some common real world issues. The vignette for arules, by the authors of the package (, ), also use a similar dataset, available within the package through data(Survey). We borrow some of their data transformations here.

We first review the dataset: there are 32,561 entities and 15 variables.

> load("survey.RData")
> dim(survey)
[1] 32561    15
> summary(survey)

      Age                   Workclass         fnlwgt       
 Min.   :17.00   Private         :22696   Min.   :  12285  
 1st Qu.:28.00   Self-emp-not-inc: 2541   1st Qu.: 117827  
 Median :37.00   Local-gov       : 2093   Median : 178356  
 Mean   :38.58   State-gov       : 1298   Mean   : 189778  
 3rd Qu.:48.00   Self-emp-inc    : 1116   3rd Qu.: 237051  
 Max.   :90.00   (Other)         :  981   Max.   :1484705  
                 NA's            : 1836                    

        Education     Education.Num                 Marital.Status 
 HS-grad     :10501   Min.   : 1.00   Divorced             : 4443  
 Some-college: 7291   1st Qu.: 9.00   Married-AF-spouse    :   23  
 Bachelors   : 5355   Median :10.00   Married-civ-spouse   :14976  
 Masters     : 1723   Mean   :10.08   Married-spouse-absent:  418  
 Assoc-voc   : 1382   3rd Qu.:12.00   Never-married        :10683  
 11th        : 1175   Max.   :16.00   Separated            : 1025  
 (Other)     : 5134                   Widowed              :  993  

           Occupation            Relationship
 Prof-specialty : 4140   Husband       :13193   Amer-Indian-Eskimo:  311  
 Craft-repair   : 4099   Not-in-family : 8305   Asian-Pac-Islander: 1039  
 Exec-managerial: 4066   Other-relative:  981   Black             : 3124  
 Adm-clerical   : 3770   Own-child     : 5068   Other             :  271  
 Sales          : 3650   Unmarried     : 3446   White             :27816  
 (Other)        :10993   Wife          : 1568                             
 NA's           : 1843                                                    

     Sex         Capital.Gain     Capital.Loss    Hours.Per.Week 
 Female:10771   Min.   :     0   Min.   :   0.0   Min.   : 1.00  
 Male  :21790   1st Qu.:     0   1st Qu.:   0.0   1st Qu.:40.00  
                Median :     0   Median :   0.0   Median :40.00  
                Mean   :  1078   Mean   :  87.3   Mean   :40.44  
                3rd Qu.:     0   3rd Qu.:   0.0   3rd Qu.:45.00  
                Max.   : 99999   Max.   :4356.0   Max.   :99.00  
                                                                 
       Native.Country  Salary.Group 
 United-States:29170   <=50K:24720  
 Mexico       :  643   >50K : 7841  
 Philippines  :  198                
 Germany      :  137                
 Canada       :  121                
 (Other)      : 1709                
 NA's         :  583

The first 5 rows of the dataset give some idea of the type of data:

> survey[1:5,]

  Age        Workclass fnlwgt Education Education.Num     Marital.Status
1  39        State-gov  77516 Bachelors            13      Never-married
2  50 Self-emp-not-inc  83311 Bachelors            13 Married-civ-spouse
3  38          Private 215646   HS-grad             9           Divorced
4  53          Private 234721      11th             7 Married-civ-spouse
5  28          Private 338409 Bachelors            13 Married-civ-spouse

         Occupation  Relationship  Race    Sex Capital.Gain Capital.Loss
1      Adm-clerical Not-in-family White   Male         2174            0
2   Exec-managerial       Husband White   Male            0            0
3 Handlers-cleaners Not-in-family White   Male            0            0
4 Handlers-cleaners       Husband Black   Male            0            0
5    Prof-specialty          Wife Black Female            0            0

  Hours.Per.Week Native.Country Salary.Group
1             40  United-States        <=50K
2             13  United-States        <=50K
3             40  United-States        <=50K
4             40  United-States        <=50K
5             40           Cuba        <=50K

The dataset contains a mixture of categoric and numeric variables while the apriori algorithm works just with categoric variables (or factors). We note that the variable fnlwgt is a calculated value and not of interest to us so we can remove it from the dataset. The variable Education.Num is redundant since is it simply a numeric mapping of Education. We can remove these from the data frame simply by assigning NULL to them:

> survey$fnlwgt <- NULL
> survey$Education.Num <- NULL

This still leaves Age, Capital.Gain, Capital.Loss, and Hours.Per.Week. Following (), we will partition Age and Hours.Per.Week into fours segments each:



> survey$Age <- ordered(cut(survey$Age, c(15, 25, 45, 65, 100)), 
               labels = c("Young", "Middle-aged", "Senior", "Old"))

> survey$Hours.Per.Week <- ordered(cut(survey$Hours.Per.Week,
                                      c(0, 25, 40, 60, 168)), 
  labels = c("Part-time", "Full-time", "Over-time", "Workaholic"))

Again following () we map Capital.Gain and Capital.Loss to None, and Low and High according to the median:



> survey$Capital.Gain <- ordered(cut(survey$Capital.Gain,
  c(-Inf, 0, median(survey$Capital.Gain[survey$Capital.Gain >0]), 1e+06)),
  labels = c("None", "Low", "High"))

> survey$Capital.Loss <- ordered(cut(survey$Capital.Loss,
  c(-Inf, 0, median(survey$Capital.Loss[survey$Capital.Loss >0]), 1e+06)), 
  labels = c("None", "Low", "High"))

That is pretty much it in terms of preparing the data for apriori:



> survey[1:5,]

          Age        Workclass Education     Marital.Status        Occupation
1 Middle-aged        State-gov Bachelors      Never-married      Adm-clerical
2      Senior Self-emp-not-inc Bachelors Married-civ-spouse   Exec-managerial
3 Middle-aged          Private   HS-grad           Divorced Handlers-cleaners
4      Senior          Private      11th Married-civ-spouse Handlers-cleaners
5 Middle-aged          Private Bachelors Married-civ-spouse    Prof-specialty

   Relationship  Race    Sex Capital.Gain Capital.Loss Hours.Per.Week
1 Not-in-family White   Male          Low         None      Full-time
2       Husband White   Male         None         None      Part-time
3 Not-in-family White   Male         None         None      Full-time
4       Husband Black   Male         None         None      Full-time
5          Wife Black Female         None         None      Full-time

  Native.Country Salary.Group
1  United-States        <=50K
2  United-States        <=50K
3  United-States        <=50K
4  United-States        <=50K
5           Cuba        <=50K

The apriori function will coerce the data into the transactions data type, and this can also be done prior to calling apriori using the as function to view the data as a transaction dataset:



> library(arules)
> survey.transactions <-  as(survey, "transactions")
> survey.transactions
transactions in sparse format with
 32561 transactions (rows) and
 115 items (columns)

This illustrates how the transactions data type represents variables in a binary form, one binary variable for each level of each categoric variable. There are 115 distinct levels (values for the categoric variables) across all 13 of the categoric variables.

The summary function provides more details:



> summary(survey.transactions)
transactions as itemMatrix in sparse format with
 32561 rows (elements/itemsets/transactions) and
 115 columns (items)

most frequent items:
           Capital.Loss = None            Capital.Gain = None 
                         31042                          29849 
Native.Country = United-States                   Race = White 
                         29170                          27816 
          Salary.Group = <=50K                        (Other) 
                         24720                         276434 

element (itemset/transaction) length distribution:
   10    11    12    13 
   27  1809   563 30162 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  10.00   13.00   13.00   12.87   13.00   13.00 

includes extended item information - examples:
             labels variables      levels
1       Age = Young       Age       Young
2 Age = Middle-aged       Age Middle-aged

The summary begins with a description of the dataset sizes. This is followed by a list of the most frequent items occurring in the dataset. A Capital.Loss of None is the single most frequent item, occurring 31,042 times (i.e., pretty much no transaction has any capital loss recorded). The length distribution of the transactions is then given, indicating that some transactions have NA's for some of the variables. Looking at the summary of the original dataset you'll see that the variables Workclass, Occupation, and Native.Country have NA's, and so the distribution ranges from 10 to 13 items in a transaction.

The final piece of information in the summary output indicates the mapping that has been used to map the categoric variables to the binary variables, so that Age = Young is one binary variable, and Age = Middle-aged is another.

Now it is time to find all association rules using apriori. After a little experimenting we have chosen a support of 0.05 and a confidence of 0.95. This gives us 4,236 rules.

> survey.rules <- apriori(survey.transactions, 
                          parameter = list(support=0.05, confidence=0.95))

parameter specification:
 confidence minval smax arem  aval originalSupport support minlen maxlen target
       0.95    0.1    1 none FALSE            TRUE    0.05      1      5  rules
   ext
 FALSE

algorithmic control:
 filter tree heap memopt load sort verbose
    0.1 TRUE TRUE  FALSE TRUE    2    TRUE

apriori - find association rules with the apriori algorithm
version 4.21 (2004.05.09)        (c) 1996-2004   Christian Borgelt
set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[115 item(s), 32561 transaction(s)] done [0.07s].
sorting and recoding items ... [36 item(s)] done [0.01s].
creating transaction tree ... done [0.08s].
checking subsets of size 1 2 3 4 5 done [0.23s].
writing ... [4236 rule(s)] done [0.00s].
creating S4 object  ... done [0.04s].



> survey.rules
set of 4236 rules



> summary(survey.rules)
set of 4236 rules

rule length distribution (lhs + rhs):
   1    2    3    4    5 
   1   34  328 1282 2591 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   4.000   5.000   4.517   5.000   5.000 

summary of quality measures:
    support          confidence          lift       
 Min.   :0.05003   Min.   :0.9500   Min.   :0.9965  
 1st Qu.:0.06469   1st Qu.:0.9617   1st Qu.:1.0186  
 Median :0.08435   Median :0.9715   Median :1.0505  
 Mean   :0.11418   Mean   :0.9745   Mean   :1.2701  
 3rd Qu.:0.13267   3rd Qu.:0.9883   3rd Qu.:1.3098  
 Max.   :0.95335   Max.   :1.0000   Max.   :2.9725

We can inspect the first 5 rules (slightly edited to suit publication):

> inspect(survey.rules[1:5])
  lhs                                 rhs                  support   conf lift
1 {}                               => {Capital.Loss = None}  0.953  0.953 1.00
2 {Occupation = Machine-op-inspct} => {Workclass = Private}  0.058  0.955 1.37
3 {Occupation = Machine-op-inspct} => {Capital.Loss = None}  0.059  0.966 1.01
4 {Race = Black}                   => {Capital.Loss = None}  0.093  0.967 1.01
5 {Occupation = Other-service}     => {Salary.Group = <=50K} 0.097  0.958 1.26

Or we can list the first 5 rules which have a lift greater that 2.5

> subset(survey.rules, subset=lift>2.5)
set of 40 rules 

> inspect(subset(survey.rules, subset=lift>2.5)[1:5])
  lhs                              rhs                         support conf lift
1 {Age = Young,                                                             
   Hours.Per.Week = Part-time} => {Marital.Status = Never-married} 0.06 0.95 2.9
2 {Age = Young,
   Relationship = Own-child}   => {Marital.Status = Never-married} 0.10 0.97 2.9
3 {Age = Young,
   Hours.Per.Week = Part-time,
   Salary.Group = <=50K}       => {Marital.Status = Never-married} 0.06 0.96 2.9
4 {Age = Young,
   Hours.Per.Week = Part-time,
   Native.Country = United-States}=>{Marital.Status=Never-married} 0.05 0.95 2.9
5 {Age = Young,
   Capital.Gain = None,
   Hours.Per.Week = Part-time} => {Marital.Status = Never-married} 0.05 0.96 2.9

Here we build quite a few more rules and then view the rule with highest lift:



> survey.rules <- apriori(survey.transactions, 
                          parameter = list(support = 0.05, confidence = 0.8))

parameter specification:
 confidence minval smax arem  aval originalSupport support minlen maxlen target
        0.8    0.1    1 none FALSE            TRUE    0.05      1      5  rules
   ext
 FALSE

algorithmic control:
 filter tree heap memopt load sort verbose
    0.1 TRUE TRUE  FALSE TRUE    2    TRUE

apriori - find association rules with the apriori algorithm
version 4.21 (2004.05.09)        (c) 1996-2004   Christian Borgelt
set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[115 item(s), 32561 transaction(s)] done [0.09s].
sorting and recoding items ... [36 item(s)] done [0.02s].
creating transaction tree ... done [0.10s].
checking subsets of size 1 2 3 4 5 done [0.35s].
writing ... [13344 rule(s)] done [0.00s].
creating S4 object  ... done [0.08s].

> inspect(SORT(subset(survey.rules, subset=rhs %in% "Salary.Group"), 
               by="lift")[1:3])

  lhs                                rhs                 support conf lift
1 {Occupation = Exec-managerial, 
    Relationship = Husband, 
    Capital.Gain = High}          => {Salary.Group = >50K} 0.007    1 4.15
2 {Age = Middle-aged, 
    Occupation = Exec-managerial, 
    Capital.Gain = High}          => {Salary.Group = >50K} 0.005    1 4.15
3 {Age = Middle-aged, 
    Education = Bachelors, 
    Capital.Gain = High}          => {Salary.Group = >50K} 0.006    1 4.15

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