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Find all frequent itemset using apriori algorithm. Assume minimum support = 40%
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written 6.4 years ago by
teamques10
★ 64k
|
| 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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