Question

Question 3 (10 marks) A large supermarket tracks sales data by stock- keeping unit (SKU) for each item, such as “pasta”, “rice”, “egg”, “soup” is identified by a numerical SKU. The supermarket has a database of transactions where each transaction is a set of SKUs that were bought together. The database of transactions consist of following itemsets:

Transaction ID Itemsets

T1 egg, soup

T2 rice, egg, soup

T3 rice, soup

T4 pasta, rice, egg, soup

T5 rice, egg

T6 pasta, rice, soup

T7 pasta, rice

Solve the above problem using Apriori Algorithm (Association Rule Mining) with minimum support of 3 and write each and every step.

Answer 1

Given minimum support is 3

Solving the question using Apriori Algorithm:

Initially, valid itemset = {“pasta”, “rice”, “egg”, “soup”}

Step-1:

Items of size-1 from itemset are as follows:

Item	Count
pasta	3
rice	6
egg	4
soup	5

Since all the items are greater than minimum support, there is no need to delete any items from itemset.

Valid itemset = {“pasta”, “rice”, “egg”, “soup”}

Step-2:

Items of size-2 from itemset are as follows:

Item	Count
pasta,rice	3
pasta,egg	1
pasta,soup	2
rice,egg	3
rice,soup	4
egg,soup	3

Since "pasta,egg" and "pasta,soup" are less than minimum support, delete them from items.

So new Valid itemset = {“pasta”, “rice”, “egg”, “soup”}

Step-3:

Items of size-3 from itemset are as follows:

Item	Count
pasta,rice,egg	1
pasta,rice,soup	2
pasta,egg.soup	1
egg,rice,soup	2

Since all the items are less than minimum support, now the itemset becomes empty.

valid itemset = {}

Since we have empty itemset, we can't proceed further.

Now group all valid itemset from step-2 to final step.

Valid frequent patterns are as follows: {{pasta,rice},{rice,egg},{rice,soup},{egg,soup}}

Mentiuon in commments if any mistakes or errors are found. Thank you.