I have a dataset which looks like this:
\"user.get\",\"search_restaurants\",\"cuisines.get\"
\"user.get\",\"search_restaurants\",\"user.get\",\"search_restaurant
The answer by luke is correct. In addition, we can say that apriori always gives us information about the consequent which is RHS in the program. That's why for a single item set having min support equal to min confidence is also given in the resulting output if no 'minlen' is used.
Eg.
> inspect(rules)
lhs rhs support confidence lift count
[1] {} => {Soup} 0.8 0.8 1.0 4
[2] {} => {Pasta} 0.8 0.8 1.0 4
[3] {Salad} => {Ham} 0.4 1.0 1.7 2
I hope this explains the output(other rules are not shown in this example). The above given is the partial output of this table.
Customer ID Food
1 -Salad, Hamburger, Taco
2 -Soup, Hamburger, Pasta
3 -Salad, Soup, Hamburger, Pasta
4 -Soup, Pasta
5 -Taco, Pasta, Soup