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Using following data, find all frequent itemset using apriori algorithm. Assume min. Support ort = 40%
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TID Items
01 A,B,C,D
02 B,C,D
03 A,B,E
04 B,D
05 A,B,C,E

Support count = 40%

x/5 * 100 = 50

x = 3

Step 1:

Generating 1-itemset frequent pattern

Scan D for count of each candidate

C1 =

Itemset Supportcount
{A} 3
{B} 5
{D} 3
{C} 2
{E} 3

Compare candidate support count with minimum support count L1

Itemset Supportcount
{A} 3
{B} 5
{D} 3
{E} 3

Step 2:

Generate C2- itemset Frequent Pattern

Generate C2 candidate from L1

C2 =

Itemset Supportcount
{A,B} 3
{A,D} 1
{A,E} 3
{B,D} 3
{B,E} 3

Compare candidate support count with minimum support count L2

Itemset
{A,B}
{A,E}
{B,D}
{B,E}

Step 3:

Generating 3- itemset Frequent Pattern

C3 =

Itemset Supportcount
{A,B,E} 2
{A,B,D} 1
{A,E,B,D} 1

Compare candidate support count with minimum support count.

As the support count generated is less than minimum support count.

So, there is no item set with minimum support count.

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