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An incremental nonparametric Bayesian clustering-based traversable region detection method

Overview of attention for article published in Autonomous Robots, July 2016
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About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
2 X users
facebook
1 Facebook page

Citations

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7 Dimensions

Readers on

mendeley
24 Mendeley
Title
An incremental nonparametric Bayesian clustering-based traversable region detection method
Published in
Autonomous Robots, July 2016
DOI 10.1007/s10514-016-9588-7
Authors

Honggu Lee, Kiho Kwak, Sungho Jo

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users 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 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 33%
Student > Master 4 17%
Student > Bachelor 3 13%
Professor 1 4%
Researcher 1 4%
Other 1 4%
Unknown 6 25%
Readers by discipline Count As %
Computer Science 10 42%
Engineering 4 17%
Linguistics 1 4%
Social Sciences 1 4%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Other 0 0%
Unknown 7 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 July 2016.
All research outputs
#14,856,861
of 22,880,230 outputs
Outputs from Autonomous Robots
#335
of 518 outputs
Outputs of similar age
#212,120
of 350,781 outputs
Outputs of similar age from Autonomous Robots
#25
of 34 outputs
Altmetric has tracked 22,880,230 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 518 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 29th percentile – i.e., 29% 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 350,781 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.