The document summarizes the Apriori algorithm for finding frequent itemsets in a transactional database. It provides an example applying the Apriori algorithm to a sample dataset with a minimum support of 2. The example performs 4 steps: 1) Finding the candidate itemsets C1 and frequent itemsets L1, 2) Generating candidate itemsets C2 and frequent itemsets L2, 3) Generating candidate itemsets C3 and frequent itemsets L3, 4) Determining the final frequent itemset is {A,C,D} with a support of 2.

1. Modern College of Arts, Science & Commerce Ganeshkhind Pune-16.
Department of Computer Science
Data Mining Assignment
THE APRIORI ALGORITHM
EXAMPLE
Presented by
Ms. Gunshri Patil gunshrisp4955@gmail.com
Ms. Divya Chavda divyachavda429@gmail.com
Ms. Dhanshree Hande dhanshreehande2157@gmail.com
Ms. Madhuri Patil patilmadhuri4488@gmail.com
Guided by
Dr. Dipali Meher
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2. Q. Perform Apriori Algorithm to Find Frequent Patterns
with Minimum Support=2.
 Given Dataset:
Tid Items
1 A,B,C,D
2 A,B,D
3 A,D
4 A,C
5 B,C
6 B,D
7 A,C,D
2

3. Step-1:Calculating C1(Candidate set)
Minimum Support=2
Dataset D
C1
C1
TID Items
1 A,B,C,D
2 A,B,D
3 A,D
4 A,C
5 B,C
6 B,D
7 A,C,D
Itemset Support Count
{A} 5
{B} 4
{C} 4
{D} 5
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4. Step-2:Now, we will take the itemsets that have the Minimum
Support(2).it will give us the table L1(frequent itemset)
C1 L1
L1
Itemset Support Count
{A} 5
{B} 4
{C} 4
{D} 5
Itemset Support Count
{A} 5
{B} 4
{C} 4
{D} 5
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5. Step-3: Candidate Generation C2, and L2:
C2 L2
Itemset
{A,B}
{A,C}
{A,D}
{B,C}
{B,D}
{C,D}
Itemset Support Count
{A,B} 2
{A,C} 3
{A,D} 4
{B,C} 2
{B,D} 3
{C,D} 2
L2
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6. Step-4: Candidate generation C3, and L3:
L3 will have only one combination, i.e., {A, C, D}.
C3 L3
 Frequent itemset ={A,C,D}
Itemset Support Count
{A,B,C} 1
{B,C,D} 1
{A,C,D} 2
{A,D,B} 0
Itemset
{A,C,D}
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7. THANK YOU
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