Frequent pattern growth
Frequent pattern growth (FP-growth) is a frequent itemset generation technique (similar to Apriori). FP-Growth builds a compact-tree structure and uses the tree for frequent itemset mining and generating rules. It is faster than Apriori and can throw results with large datasets.
Let's go through the steps of FP-Growth:
- Setting up the transactions: This step sets up the items by frequency. However, the items are set up vertically, not horizontally. That means transforming input from transaction to items:
t_id | Items |
1 | (B, C, D, A) |
2 | (B, C, D) |
3 | (D, A) |
4 | (A, B) |
5 | (A, C, B) |
- Finding the frequency: Now we have to find out the frequency of each item individually:
Items | Frequency |
A | 4 |
B | 4 |
C | 3 |
D | 3 |
Let's set up the minimum threshold or minimum support as 50%:
- Min Support = (5*50/100) = 2.5
- Ceiling of minimum support = 2.5 ~ 3
- Prioritize the items by frequency: Since all the items have a frequency greater than or equal to minimum support, all the items will be part of it. Also, based on their frequency,...
