Question

I’ve been doing some work for my exams in a few days and I’m going through some past papers but unfortunately there are no corresponding answers. I’ve answered the question and I was wondering if someone could tell me if I am correct.

My question is

(c) A transactional dataset, T, is given below:

t1: Milk, Chicken, Beer

t2: Chicken, Cheese

t3: Cheese, Boots

t4: Cheese, Chicken, Beer,

t5: Chicken, Beer, Clothes, Cheese, Milk

t6: Clothes, Beer, Milk

t7: Beer, Milk, Clothes

Assume that minimum support is 0.5 (minsup = 0.5).

(i) Find all frequent itemsets.

Here is how I worked it out:

Item : Amount

Milk : 4

Chicken : 4

Beer : 5

Cheese : 4

Boots : 1

Clothes : 3

Now because the minsup is 0.5 you eliminate boots and clothes and make a combo of the remaining giving:

{items} : Amount

{Milk, Chicken} : 2

{Milk, Beer} : 4

{Milk, Cheese} : 1

{Chicken, Beer} : 3

{Chicken, Cheese} : 3

{Beer, Cheese} : 2

Which leaves milk and beer as the only frequent item set then as it is the only one above the minsup?

Answer 1

There are two ways to solve the problem:

1. using Apriori algorithm
2. Using FP counting

Assuming that you are using Apriori, the answer you got is correct.

The algorithm is simple:
First you count frequent 1-item sets and exclude the item-sets below minimum support.

Then count frequent 2-item sets by combining frequent items from previous iteration and exclude the item-sets below support threshold.
The algorithm can go on until no item-sets are greater than threshold.

In the problem given to you, you only get 1 set of 2 items greater than threshold so you can’t move further.

There is a solved example of further steps on Wikipedia here.

You can refer “Data Mining Concepts and Techniques” by Han and Kamber for more examples.