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FIMI
2003
95views Data Mining» more  FIMI 2003»
13 years 6 months ago
Probabilistic Iterative Expansion of Candidates in Mining Frequent Itemsets
A simple new algorithm is suggested for frequent itemset mining, using item probabilities as the basis for generating candidates. The method first finds all the frequent items, an...
Attila Gyenesei, Jukka Teuhola
AUSAI
2001
Springer
13 years 9 months ago
Further Pruning for Efficient Association Rule Discovery
The Apriori algorithm's frequent itemset approach has become the standard approach to discovering association rules. However, the computation requirements of the frequent item...
Songmao Zhang, Geoffrey I. Webb
IEEEHPCS
2010
13 years 3 months ago
Discovering closed frequent itemsets on multicore: Parallelizing computations and optimizing memory accesses
The problem of closed frequent itemset discovery is a fundamental problem of data mining, having applications in numerous domains. It is thus very important to have efficient par...
Benjamin Négrevergne, Alexandre Termier, Je...
INAP
2001
Springer
13 years 9 months ago
Discovering Frequent Itemsets in the Presence of Highly Frequent Items
This paper presents new techniques for focusing the discoveryof frequent itemsets within large, dense datasets containing highly frequent items. The existence of highly frequent i...
Dennis P. Groth, Edward L. Robertson
FIMI
2003
120views Data Mining» more  FIMI 2003»
13 years 6 months ago
MAFIA: A Performance Study of Mining Maximal Frequent Itemsets
We present a performance study of the MAFIA algorithm for mining maximal frequent itemsets from a transactional database. In a thorough experimental analysis, we isolate the effec...
Douglas Burdick, Manuel Calimlim, Jason Flannick, ...