Geodesic
An Efficient Procedure for Mining Statistically Significant Frequent Itemsets
Publications de l'Institut Mathématique, _N_S_87 (2010) no. 101, p. 109 .

Voir la notice de l'article provenant de la source eLibrary of Mathematical Institute of the Serbian Academy of Sciences and Arts

We suggest the original procedure for frequent itemsets generation, which is more efficient than the appropriate procedure of the well known Apriori algorithm. The correctness of the procedure is based on a special structure called Rymon tree. For its implementation, we suggest a modified sort-merge-join algorithm. Finally, we explain how the support measure, which is used in Apriori algorithm, gives statistically significant frequent itemsets.
Classification : 03B70 68T27 68Q17
Keywords: data mining, knowledge discovery in databases, association analysis, Apriori algorithm 
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     author = {Predrag Stani\v{s}i\'c and Savo Tomovi\'c},
     title = {An {Efficient} {Procedure} for {Mining} {Statistically} {Significant} {Frequent} {Itemsets}},
     journal = {Publications de l'Institut Math\'ematique},
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Predrag Stanišić; Savo Tomović. An Efficient Procedure for Mining Statistically Significant Frequent Itemsets. Publications de l'Institut Mathématique, _N_S_87 (2010) no. 101, p. 109 . http://geodesic.mathdoc.fr/item/PIM_2010_N_S_87_101_a7/