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Mendeley readers
Chapter title |
Efficient Mining of Combined Subspace and Subgraph Clusters in Graphs with Feature Vectors
|
---|---|
Chapter number | 22 |
Book title |
Advances in Knowledge Discovery and Data Mining
|
Published by |
Springer Berlin Heidelberg, April 2013
|
DOI | 10.1007/978-3-642-37453-1_22 |
Book ISBNs |
978-3-64-237452-4, 978-3-64-237453-1
|
Authors |
Stephan Günnemann, Brigitte Boden, Ines Färber, Thomas Seidl |
Editors |
Jian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Sweden | 1 | 5% |
China | 1 | 5% |
Estonia | 1 | 5% |
France | 1 | 5% |
Unknown | 16 | 80% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 7 | 35% |
Student > Ph. D. Student | 5 | 25% |
Professor > Associate Professor | 2 | 10% |
Professor | 1 | 5% |
Student > Bachelor | 1 | 5% |
Other | 1 | 5% |
Unknown | 3 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 13 | 65% |
Mathematics | 3 | 15% |
Medicine and Dentistry | 1 | 5% |
Unknown | 3 | 15% |