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Efficient Adaptive-Support Association Rule Mining for Recommender Systems

Overview of attention for article published in Data Mining and Knowledge Discovery, January 2002
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Mentioned by

patent
2 patents

Citations

dimensions_citation
307 Dimensions

Readers on

mendeley
170 Mendeley
citeulike
16 CiteULike
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Title
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
Published in
Data Mining and Knowledge Discovery, January 2002
DOI 10.1023/a:1013284820704
Authors

Weiyang Lin, Sergio A. Alvarez, Carolina Ruiz

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 170 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
France 2 1%
Germany 2 1%
United Kingdom 2 1%
Malaysia 1 <1%
Brazil 1 <1%
Portugal 1 <1%
Hungary 1 <1%
Tunisia 1 <1%
Other 6 4%
Unknown 151 89%

Demographic breakdown

Readers by professional status Count As %
Student > Master 37 22%
Student > Ph. D. Student 36 21%
Researcher 22 13%
Student > Bachelor 16 9%
Professor > Associate Professor 13 8%
Other 24 14%
Unknown 22 13%
Readers by discipline Count As %
Computer Science 107 63%
Business, Management and Accounting 11 6%
Engineering 8 5%
Economics, Econometrics and Finance 3 2%
Social Sciences 3 2%
Other 8 5%
Unknown 30 18%
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 11 June 2019.
All research outputs
#8,534,976
of 25,373,627 outputs
Outputs from Data Mining and Knowledge Discovery
#175
of 637 outputs
Outputs of similar age
#32,444
of 130,776 outputs
Outputs of similar age from Data Mining and Knowledge Discovery
#1
of 1 outputs
Altmetric has tracked 25,373,627 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 637 research outputs from this source. They receive a mean Attention Score of 3.7. This one has gotten more attention than average, scoring higher than 58% 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 130,776 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 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