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Missing value imputation: a review and analysis of the literature (2006–2017)

Overview of attention for article published in Artificial Intelligence Review, April 2019
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1 X user

Citations

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301 Dimensions

Readers on

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389 Mendeley
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Title
Missing value imputation: a review and analysis of the literature (2006–2017)
Published in
Artificial Intelligence Review, April 2019
DOI 10.1007/s10462-019-09709-4
Authors

Wei-Chao Lin, Chih-Fong Tsai

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 389 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 389 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 49 13%
Student > Ph. D. Student 43 11%
Researcher 23 6%
Student > Bachelor 22 6%
Lecturer 17 4%
Other 42 11%
Unknown 193 50%
Readers by discipline Count As %
Computer Science 70 18%
Engineering 36 9%
Mathematics 12 3%
Business, Management and Accounting 8 2%
Biochemistry, Genetics and Molecular Biology 8 2%
Other 46 12%
Unknown 209 54%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 05 April 2019.
All research outputs
#15,568,386
of 23,140,503 outputs
Outputs from Artificial Intelligence Review
#410
of 718 outputs
Outputs of similar age
#219,780
of 352,213 outputs
Outputs of similar age from Artificial Intelligence Review
#2
of 16 outputs
Altmetric has tracked 23,140,503 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 718 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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 352,213 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.