How to find frequent patterns?
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.
Year of publication: |
2005-06-01
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Authors: | Pijls, W.H.L.M. ; Koster, W.A. |
Institutions: | Erasmus University Rotterdam, Econometric Institute |
Saved in:
freely available
Extent: | application/pdf |
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Series: | Econometric Institute Report. - ISSN 1566-7294. |
Type of publication: | Book / Working Paper |
Notes: | The text is part of a series RePEc:dgr:eureir Number EI 2005-24 |
Source: |
Persistent link: https://www.econbiz.de/10005450874
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