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Gaussian parsimonious clustering models with covariates and a noise component

Overview of attention for article published in Advances in Data Analysis and Classification, September 2019
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
2 Mendeley
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Title
Gaussian parsimonious clustering models with covariates and a noise component
Published in
Advances in Data Analysis and Classification, September 2019
DOI 10.1007/s11634-019-00373-8
Authors

Keefe Murphy, Thomas Brendan Murphy

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 50%
Student > Master 1 50%
Readers by discipline Count As %
Agricultural and Biological Sciences 1 50%
Economics, Econometrics and Finance 1 50%

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 22 September 2019.
All research outputs
#3,914,769
of 14,277,499 outputs
Outputs from Advances in Data Analysis and Classification
#8
of 47 outputs
Outputs of similar age
#92,816
of 261,872 outputs
Outputs of similar age from Advances in Data Analysis and Classification
#2
of 5 outputs
Altmetric has tracked 14,277,499 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 47 research outputs from this source. They receive a mean Attention Score of 2.0. This one scored the same or higher as 39 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 261,872 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 64% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.