–What is DFI-Growth?
DFI-Growth is an algorithm for deriving frequent itemsets from frequent closed itemsets by pattern growth.
–What is the input of the DFI-Growth algorithm?
The input of DFI-Growth is a FCI database.
A FCI database is a set of frequent closed itemsets.
For example, consider the following FCI database. It contains 6 frequent closed itemsets and support number of each itemset. This database is provided as the file contextMushroom_FCI90.txt.
This input file was obtained by applying the Charm algorithm (proposed by Zaki) on the Mushroom.txt dataset with 90% as the minsup threshold.
| frequent closed itemsets | support |
|---|---|
| {36 90 97} | 7576 |
| {90 97} | 7768 |
| {36 90 94} | 8192 |
| {36 90} | 8200 |
| {90 94} | 8216 |
| {90} | 8416 |
–What is the output of the DFI-Growth algorithm?
DFI-Growth is an algorithm for deriving frequent itemsets from frequent closed itemsets.
A frequent itemset is an itemset which appears in at least minsup transactions from the transaction database. And a frequent closed itemset is a frequent itemset that none of its immediate supersets have the same support number as itself.
For example, if DFI-Growth is run on the previous FCI database, DFI-Growth produces the following result:
| frequent closed itemsets | support |
|---|---|
| {36 97} | 7576 |
| {36 90 97} | 7576 |
| {90 97} | 7768 |
| {97} | 7768 |
| {36 94} | 8192 |
| {36 90 94} | 8192 |
| {90 94} | 8216 |
| {94} | 8216 |
| {36} | 8200 |
| {36 90} | 8200 |
| {90} | 8416 |
More details about DFI-Growth please refer to SPMF