Multi-Sorted Inverse Frequent Itemsets Mining for Generating Realistic No-SQL Datasets (Discussion Paper) - researchr publication related

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Domenico Saccà, Edoardo Serra, Antonino Rullo. Multi-Sorted Inverse Frequent Itemsets Mining for Generating Realistic No-SQL Datasets (Discussion Paper). In Sergio Greco, Maurizio Lenzerini, Elio Masciari, Andrea Tagarelli, editors, Proceedings of the 29th Italian Symposium on Advanced Database Systems, SEBD 2021, Pizzo Calabro (VV), Italy, September 5-9, 2021. Volume 2994 of CEUR Workshop Proceedings, pages 347-354, CEUR-WS.org, 2021. [doi]

The following publications are possibly variants of this publication:

Extending inverse frequent itemsets mining to generate realistic datasets: complexity, accuracy and emerging applicationsDomenico Saccà, Edoardo Serra, Antonino Rullo. datamine, 33(6):1736-1774, 2019. [doi]

Multi-Sorted Inverse Frequent Itemsets Mining: On-Going ResearchDomenico Saccà, Edoardo Serra, Antonio Piccolo. amw 2016: [doi]

A Decomposition Technique for the Inverse Frequent Itemset Mining ProblemAntonella Guzzo, Luigi Moccia, Domenico Saccà, Edoardo Serra. sebd 2011: 66-75

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