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Concept Decompositions for Large Sparse Text Data Using Clustering

Overview of attention for article published in Machine Learning, January 2001
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Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
870 Dimensions

Readers on

mendeley
450 Mendeley
citeulike
4 CiteULike
connotea
1 Connotea
Title
Concept Decompositions for Large Sparse Text Data Using Clustering
Published in
Machine Learning, January 2001
DOI 10.1023/a:1007612920971
Authors

Inderjit S. Dhillon, Dharmendra S. Modha

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 450 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 14 3%
Germany 6 1%
France 5 1%
Italy 5 1%
Brazil 4 <1%
United Kingdom 3 <1%
Spain 3 <1%
Turkey 2 <1%
China 2 <1%
Other 16 4%
Unknown 390 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 130 29%
Student > Master 81 18%
Researcher 59 13%
Student > Bachelor 43 10%
Student > Doctoral Student 25 6%
Other 70 16%
Unknown 42 9%
Readers by discipline Count As %
Computer Science 248 55%
Engineering 48 11%
Business, Management and Accounting 22 5%
Mathematics 13 3%
Agricultural and Biological Sciences 12 3%
Other 44 10%
Unknown 63 14%
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 17 June 2015.
All research outputs
#8,535,684
of 25,374,917 outputs
Outputs from Machine Learning
#344
of 1,225 outputs
Outputs of similar age
#26,248
of 114,350 outputs
Outputs of similar age from Machine Learning
#5
of 6 outputs
Altmetric has tracked 25,374,917 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 1,225 research outputs from this source. They receive a mean Attention Score of 4.2. 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 114,350 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.