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We also benchmarked our algorithm in comparison with well known, widely used programs: GZIP and ZLIB.
Apart from the proposed Apriori algorithm, we also benchmarked it with other algorithms for mining association rules, including traditional Apriori [46], Eclat [47], MapReduce-based Apriori (MRA) [48], and Spark-based Apriori (SBA) [49] whose implementations are summarized at [50].
We also benchmarked against two methods that operate on single data types (either interaction network or gene expression data).
We also benchmarked VAAST 2.0 on the Dallas Heart Study dataset Romeo et al., 2009, in which rare variants in ANGPTL4 gene were found to be associated with low triglyceride levels within 3,551 sequenced individuals.
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We also benchmark efficiency (i.e. error as a function of computing time) against the SPME method, which indicates that our method is competitive.
We also benchmark our performance on the standard VISOB database, where we outperform the state of the art methods, achieving a TPR of 99.5% at a FPR of 0.001%.
We have also benchmarked successfully with variable viscosity convection for lateral viscosity contrast up to 108.
We have also benchmarked our identified ghost cities with existing rankings, the Baidu searching engine and night-time light images.
And just for fun we're also benchmarking the Merom-based MacBook and a Mac Pro.
The proposed idea is also benchmarked with the state-of-the-art star pattern recognition techniques.
This model is also benchmarked with a published numerical dynamo model solution.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com