written 6.4 years ago by
teamques10
★ 64k
|
• modified 6.4 years ago |
A database has four transitions. Let minimum support and confidence is 50%
D=
| $T_id$ | Item | |||
|---|---|---|---|---|
| 100 | 1 | 3 | 4 | |
| 200 | 2 | 3 | 5 | 5 |
| 300 | 1 | 2 | 3 | |
| 400 | 2 | 5 | ||
| 500 | 1 | 2 | 3 | |
| 600 | 3 | 5 | ||
| 700 | 1 | 2 | 3 | 5 |
| 800 | 1 | 5 | ||
| 900 | 1 | 3 |
Mumbai University > Computer Engineering > sem 8> data warehouse and mining
Marks: 10M
|
written 6.4 years ago by
teamques10
★ 64k
|
• modified 6.4 years ago |
Support count = 50%
x/9 * 100 = 50
x= 4.5
Step 1:
Generating 1-itemset frequent pattern
Scan D for count of each candidate C1
| Itemset | Supportcount |
|---|---|
| {1} | 6 |
| {2} | 5 |
| {3} | 7 |
| {4} | 1 |
| {5} | 6 |
Compare candidate support count with minimum support count L1
| Itemset | Supportcount |
|---|---|
| {1} | 6 |
| {2} | 5 |
| {3} | 7 |
| {5} | 6 |
Step 2:
Generate C2- itemset Frequent Pattern
Generate C2 candidate from L1
C2 =
| Itemset |
|---|
| {1,2} |
| {1,3} |
| {1,5} |
| {2,3} |
| {2,5} |
| {3,5} |
Scan D for count of each candidate C2
| Itemset | Supportcount |
|---|---|
| {1,2} | 3 |
| {1,3} | 5 |
| {1,5} | 3 |
| {2,3} | 4 |
| {2,5} | 4 |
| {3,5} | 4 |
Compare candidate support count with minimum support count L2
| Itemset |
|---|
| {1,3} |
Step 3:
Generating 3- itemset Frequent Pattern
C3 =
| Itemset | Supportcount |
|---|---|
| {1,3} | 5 |
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