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A scalable association rule learning heuristic for large datasets

Overview of attention for article published in Journal of Big Data, June 2021
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
15 Dimensions

Readers on

mendeley
19 Mendeley
Title
A scalable association rule learning heuristic for large datasets
Published in
Journal of Big Data, June 2021
DOI 10.1186/s40537-021-00473-3
Authors

Haosong Li, Phillip C.-Y. Sheu

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 11%
Student > Postgraduate 2 11%
Student > Ph. D. Student 2 11%
Lecturer 1 5%
Student > Doctoral Student 1 5%
Other 3 16%
Unknown 8 42%
Readers by discipline Count As %
Computer Science 9 47%
Agricultural and Biological Sciences 1 5%
Economics, Econometrics and Finance 1 5%
Unknown 8 42%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 April 2024.
All research outputs
#8,647,532
of 25,654,566 outputs
Outputs from Journal of Big Data
#157
of 396 outputs
Outputs of similar age
#179,449
of 460,219 outputs
Outputs of similar age from Journal of Big Data
#11
of 20 outputs
Altmetric has tracked 25,654,566 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 396 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.5. This one has gotten more attention than average, scoring higher than 56% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 460,219 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.