R- Is there a way to limit apriori rules by lift?

Question

I'm looking at this data set: https://archive.ics.uci.edu/ml/machine-learning-databases/credit-screening/crx.data

I preprocessed the data:

ca.1<-read.csv("CreditApproval.csv",T,",")

# From http://stackoverflow.com/q/4787332/
remove_outliers <- function(x, na.rm = TRUE, ...) {
  qnt <- quantile(x, probs=c(.25, .75), na.rm = na.rm, ...)
  H <- 1.5 * IQR(x, na.rm = na.rm)
  y <- x
  y[x < (qnt[1] - H)] <- NA
  y[x > (qnt[2] + H)] <- NA
  y
}

ca.1$A2<-remove_outliers(ca$A2)
ca.1$A3<-remove_outliers(ca$A3)
ca.1$A8<-remove_outliers(ca$A8)
ca.1$A11<-remove_outliers(ca$A11)
ca.1$A14<-remove_outliers(ca$A14)
ca.1$A15<-remove_outliers(ca$A15)
ca.1$A2<-discretize(ca.1$A2,"frequency",categories = 6)
ca.1$A3<-discretize(ca.1$A3,"frequency",categories = 6)
ca.1$A8<-discretize(ca.1$A8,"frequency",categories = 6)
ca.1$A11<-discretize(ca.1$A11,"frequency",categories = 6)
ca.1$A14<-discretize(ca.1$A14,"frequency",categories = 6)
ca.1$A15<-discretize(ca.1$A15,"frequency",categories = 6)

ca.1<-na.omit(ca.1)

After fine tuning the support, confidence, min/maxlen I'm still getting 65 rules:

> rules<-apriori(ca.1, parameter= list(supp=0.15, conf=0.89, minlen=3, maxlen=4), appearance=list(rhs=c("class=-", "class=+"), default="lhs"))
> rules.sorted <- sort(rules, by="lift")
> inspect(rules.sorted)
     lhs                     rhs       support   confidence lift    
[1]  {A5=g,A9=t,A10=t}    => {class=+} 0.1521739 0.8974359  2.770607
[2]  {A4=u,A9=t,A10=t}    => {class=+} 0.1521739 0.8974359  2.770607
[3]  {A1=a,A9=f}          => {class=-} 0.1717391 0.9753086  1.442579
[4]  {A1=a,A9=f,A13=g}    => {class=-} 0.1608696 0.9736842  1.440176
...[65]

As you can see + rules have a greater lift, but less support and confidence than the - rules. I've been looking through the docs, and can't find any parameter to limit by lift. Is this possible? If not, what do you do in situations like this?

are you looking for that? subrules <- subset(rules, lift>threshold) — MFR, Oct 14 '16 at 01:54
This example is not reproducible. http://stackoverflow.com/help/mcve — Hack-R, Oct 14 '16 at 02:56
@MFR I was wondering if the apriori method offered a way to accomplish this. I mean is this not a common issue? — Travis Heeter, Oct 14 '16 at 12:36

score 5 · Accepted Answer · answered Feb 15 '19 at 16:58

5

In arules package a special function to subset this object type is defined. In order to filter out rules with lift value less than 2 you can try the following:

subset(rules, subset = lift > 2)

answered Feb 15 '19 at 16:58

Denys

66
1
3

score 2 · Answer 2 · answered Apr 08 '19 at 11:19

You can't limit apriori rules by lift alone. You have to get a limit by support and confidence first which you did here:

 rules<-apriori(ca.1, parameter= list(supp=0.15, conf=0.89, minlen=3, maxlen=4)

Then after that, do something like this

rulesLift <- sort(subset(rules, subset = lift < 2), by="lift") 
inspect(rulesLift)

score 2 · Answer 3 · answered Jun 03 '20 at 18:27

Another way is to use arules::quality(). For example:

association.rules <- apriori(tr, parameter = list(support=0.005, confidence=0.25, minlen=3, maxlen=10))

subRules<-association.rules[quality(association.rules)$lift > 1]

This function can filter by support, confidence, coverage, lift, count.

score 0 · Answer 4 · answered Apr 12 '18 at 15:55

I think apriori function does not take lift as one of the parameter. I get this error if I try to set lift

Error: Invalid parameter: lift

Instead I could sort the rules by lift and pick the rules based on the lift value as follows

sort (rules, by="lift", decreasing=TRUE)

This is not a straightforward solution but a decent workaround

score -1 · Answer 5 · answered Mar 22 '17 at 14:43

-1

What if you tried:

apriori(df, parameter = list(lift = 0.3, minlen =2))

You can set your minimum lift to anything in this case, just chose 0.3.

answered Mar 22 '17 at 14:43

nak5120

4,089
4
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Hi. There is no lift option to be set as a parameter. – Abdinardo Oliveira May 25 '21 at 17:14

R- Is there a way to limit apriori rules by lift?

5 Answers5