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Semi-random partitioning of data into training and test sets in granular computing context

Overview of attention for article published in Granular Computing, August 2017
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)

Mentioned by

twitter
1 X user
patent
1 patent

Citations

dimensions_citation
104 Dimensions

Readers on

mendeley
127 Mendeley
Title
Semi-random partitioning of data into training and test sets in granular computing context
Published in
Granular Computing, August 2017
DOI 10.1007/s41066-017-0049-2
Authors

Han Liu, Mihaela Cocea

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 127 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 127 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 20 16%
Student > Bachelor 18 14%
Student > Ph. D. Student 15 12%
Researcher 7 6%
Lecturer 4 3%
Other 9 7%
Unknown 54 43%
Readers by discipline Count As %
Computer Science 22 17%
Engineering 16 13%
Agricultural and Biological Sciences 4 3%
Business, Management and Accounting 4 3%
Mathematics 3 2%
Other 14 11%
Unknown 64 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 09 August 2022.
All research outputs
#6,667,976
of 23,560,187 outputs
Outputs from Granular Computing
#1
of 1 outputs
Outputs of similar age
#104,942
of 319,032 outputs
Outputs of similar age from Granular Computing
#1
of 1 outputs
Altmetric has tracked 23,560,187 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 1 research outputs from this source. They receive a mean Attention Score of 0.0. This one scored the same or higher as 0 of them.
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 319,032 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 66% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them