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Mendeley readers
| Title |
Semantic interestingness measures for discovering association rules in the skeletal dysplasia domain
|
|---|---|
| Published in |
Journal of Biomedical Semantics, February 2014
|
| DOI | 10.1186/2041-1480-5-8 |
| Pubmed ID | |
| Authors |
Razan Paul, Tudor Groza, Jane Hunter, Andreas Zankl |
| Abstract |
Lately, ontologies have become a fundamental building block in the process of formalising and storing complex biomedical information. With the currently existing wealth of formalised knowledge, the ability to discover implicit relationships between different ontological concepts becomes particularly important. One of the most widely used methods to achieve this is association rule mining. However, while previous research exists on applying traditional association rule mining on ontologies, no approach has, to date, exploited the advantages brought by using the structure of these ontologies in computing rule interestingness measures. |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Canada | 1 | 3% |
| Unknown | 27 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 28% |
| Student > Master | 5 | 17% |
| Researcher | 4 | 14% |
| Student > Bachelor | 3 | 10% |
| Professor > Associate Professor | 3 | 10% |
| Other | 4 | 14% |
| Unknown | 2 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 18 | 62% |
| Medicine and Dentistry | 3 | 10% |
| Engineering | 3 | 10% |
| Chemistry | 1 | 3% |
| Design | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 3 | 10% |