InforLorV4, Main, Merge, bibRecord, 003F55

An Efficient Hybrid Algorithm for Mining Frequent Closures and Generators

Identifieur interne : 003F55 ( Main/Merge ); précédent : 003F54; suivant : 003F56

An Efficient Hybrid Algorithm for Mining Frequent Closures and Generators

Auteurs : Laszlo Szathmary [Canada] ; Petko Valtchev [Canada] ; Amedeo Napoli [France] ; Robert Godin [Canada]

Source :

RBID : Hal:inria-00335247

English descriptors

mix :
- algorithms, association rule extraction, data mining, frequent closed itemsets, frequent generators, frequent itemsets, itemset extraction, knowledge discovery in databases (KDD).

Abstract

The effective construction of many association rule bases requires the computation of both frequent closed and frequent generator itemsets (FCIs/FGs). However, these two tasks are rarely combined. Most of the existing solutions apply levelwise breadth-first traversal, though depth-first traversal, depending on data characteristics, is often superior. Hence, we address here a hybrid algorithm that combines the two different traversals. The proposed algorithm, Eclat-Z, extracts frequent itemsets (FIs) in a depth-first way. Then, the algorithm filters FCIs and FGs among FIs in a levelwise manner, and associates the generators to their closures. In Eclat-Z we present a generic technique for extending an arbitrary FI-miner algorithm in order to support the generation of minimal non-redundant association rules too. Experimental results indicate that Eclat-Z outperforms pure levelwise methods in most cases.

Url:

https://hal.inria.fr/inria-00335247

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 000C77
to stream Hal, to step Curation: 000C77
to stream Hal, to step Checkpoint: 003137

Links to Exploration step

Hal:inria-00335247

Le document en format XML

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<front><div type="abstract" xml:lang="en">The effective construction of many association rule bases requires the computation of both frequent closed and frequent generator itemsets (FCIs/FGs). However, these two tasks are rarely combined. Most of the existing solutions apply levelwise breadth-first traversal, though depth-first traversal, depending on data characteristics, is often superior. Hence, we address here a hybrid algorithm that combines the two different traversals. The proposed algorithm, Eclat-Z, extracts frequent itemsets (FIs) in a depth-first way. Then, the algorithm filters FCIs and FGs among FIs in a levelwise manner, and associates the generators to their closures. In Eclat-Z we present a generic technique for extending an arbitrary FI-miner algorithm in order to support the generation of minimal non-redundant association rules too. Experimental results indicate that Eclat-Z outperforms pure levelwise methods in most cases.</div>
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An Efficient Hybrid Algorithm for Mining Frequent Closures and Generators

An Efficient Hybrid Algorithm for Mining Frequent Closures and Generators

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