How to find frequent patterns?
June 2005
Research Paper
This publication is part of collection
| Related Files |
|---|
View PDF Version
(ei2005-24.pdf, 0.2MB) |
An improved version of DF, the depth-first implementation of Apriori, is presented. Given a database of (e.g., supermarket) transactions, the DF algorithm builds a so-called trie that contains all frequent itemsets, i.e., all itemsets that are contained in at least `minsup' transactions with `minsup' a given threshold value. In the trie, there is a one-to-one correspondence between the paths and the frequent itemsets. The new version, called DF+, differs from DF in that its data structure representing the database is borrowed from the FP-growth algorithm. So it combines the compact FP-growth data structure with the efficient trie-building method in DF.
Automatically Extracted Terms
- database
- itemset
- transaction
- algorithm
- figure
- support
- fp-tree
- fp-growth
- pattern
- number
- execution
- fp-growth algorithm
- data structure
- right
- result
- mining
- memory
- right axis
- implementation
- figure 3
