Extending inverse frequent itemsets mining to generate realistic datasets: complexity, accuracy and emerging applications

Saccà, Domenico; Serra, Edoardo; Rullo, Antonino

doi:10.1007/s10618-019-00643-1

Cited by 4 publications

(22 citation statements)

References 36 publications

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“…As future work, an interesting research line is trying to investigate and define novel methodologies and techniques that are able to take advantage of IFM and PGMs, by combining their strong points and mitigating their weakness. Another promising research line is to apply the combination of the two approaches to NoSQL applications by considering the extension of IFM that has been recently proposed in (Saccà et al, 2019). This extension considers more structured schemes for the datasets to be generated, as required by emerging big data applications, for example, social network analytics.…”

Section: Discussionmentioning

confidence: 99%

“…Saccà et al (2019) have shown that the extended column generation algorithm can handle linear programs with an enormous number of variables and constraints (from

10^{22}

to over

10^{240}

in their experiments) using a reduced amount of space. These experiments reveal that time does not grow exponentially in practice as it often happens for the classical execution of the simplex algorithm.…”

Section: Inverse Frequent Itemset Mining‐based Generative Modelsmentioning

confidence: 99%

“…Finally, as shown in (Saccà et al, 2019), the extended column generation algorithm can be easily specialized to solve all versions of the general problem but

{IFM}_{s}

, in particular:

{IFM}_{I}

is modeled by LP if the inequalities () are removed. Then, the extension of the column generation algorithm to handle bounded variables is not necessary anymore and, then, the algorithm reduces to the one proposed in (Guzzo et al, 2013).…”

Section: Inverse Frequent Itemset Mining‐based Generative Modelsmentioning

confidence: 99%

“…The complexity of the decision version of IFM G has been proved to be NEXP-complete in (Saccà et al, 2019). Next, we summarize the complexity of the decision versions of all formulations of IFM:…”

Section: General Formulation Of Ifmmentioning

confidence: 99%

“…In this section, we present the general formulation of IFM given in (Saccà et al, 2019) that takes into account both infrequency and duplicate constraints. Let us first recap and extend the notation introduced at the beginning of this section:…”

Section: General Formulation Of Ifmmentioning

confidence: 99%

See 4 more Smart Citations

Machine learning methods for generating high dimensional discrete datasets

Manco

Ritacco

Rullo

et al. 2022

WIREs Data Min & Knowl

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The development of platforms and techniques for emerging Big Data and Machine Learning applications requires the availability of real‐life datasets. A possible solution is to synthesize datasets that reflect patterns of real ones using a two‐step approach: first, a real dataset X is analyzed to derive relevant patterns Z and, then, to use such patterns for reconstructing a new dataset X′ that preserves the main characteristics of X. This survey explores two possible approaches: (1) Constraint‐based generation and (2) probabilistic generative modeling. The former is devised using inverse mining (IFM) techniques, and consists of generating a dataset satisfying given support constraints on the itemsets of an input set, that are typically the frequent ones. By contrast, for the latter approach, recent developments in probabilistic generative modeling (PGM) are explored that model the generation as a sampling process from a parametric distribution, typically encoded as neural network. The two approaches are compared by providing an overview of their instantiations for the case of discrete data and discussing their pros and cons. This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Machine Learning Algorithmic Development > Structure Discovery

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Section: Discussionmentioning

confidence: 99%

“…Saccà et al (2019) have shown that the extended column generation algorithm can handle linear programs with an enormous number of variables and constraints (from

10^{22}

to over

10^{240}

Section: Inverse Frequent Itemset Mining‐based Generative Modelsmentioning

confidence: 99%

“…Finally, as shown in (Saccà et al, 2019), the extended column generation algorithm can be easily specialized to solve all versions of the general problem but