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

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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/