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A scientist's impact over time: The predictive power of clustering with peers
dc.creator | Gogoglou A., Sidiropoulos A., Katsaros D., Manolopoulos Y. | en |
dc.date.accessioned | 2023-01-31T07:43:27Z | |
dc.date.available | 2023-01-31T07:43:27Z | |
dc.date.issued | 2016 | |
dc.identifier | 10.1145/2938503.2938523 | |
dc.identifier.isbn | 9781450341189 | |
dc.identifier.uri | http://hdl.handle.net/11615/72559 | |
dc.description.abstract | The identification of latent patterns in big scholarly data that concern the performance of researchers is a significant task because it can potentially impact scien-tific careers since they are based in funding and promo-tion. This article investigates the temporal evolution of a scientist's impact. Instead of taking a detailed, microscopic view that examines the citation curves of every scientist's article, the article develops a scalable, macroscopicmethodology that uses the articles' citation profiles to build a more abstract and high-level profile that characterizes a scientist. This profile is utilized to cluster scientists in a set of 'performance' clusters. To this end, established techniques such as Principal Com-ponent Analysis and Self-OrganizingMap clustering are employed as well as a set of proposed heuristics. The effectiveness of the proposed methodology is examined by comparing the resulting rankings with the outcomes of the peer-review procedures that resulted in the E. F. Codd and the Turing awards. The good match be-tween the outcomes of computerized and peer-review procedures provides solid evidence that the proposed techniques constitute a promising analysis method for big scholarly data. © ACM 2016. | en |
dc.language.iso | en | en |
dc.source | ACM International Conference Proceeding Series | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84989244223&doi=10.1145%2f2938503.2938523&partnerID=40&md5=c2bf073b30df4a71558f97ea6d505ab1 | |
dc.subject | Abstracting | en |
dc.subject | Biographies | en |
dc.subject | Conformal mapping | en |
dc.subject | Data reduction | en |
dc.subject | Database systems | en |
dc.subject | Self organizing maps | en |
dc.subject | Analysis method | en |
dc.subject | Career paths | en |
dc.subject | Clustering | en |
dc.subject | H indices | en |
dc.subject | Microscopic views | en |
dc.subject | Perfectionismindex | en |
dc.subject | Predictive power | en |
dc.subject | Temporal evolution | en |
dc.subject | Principal component analysis | en |
dc.subject | Association for Computing Machinery | en |
dc.title | A scientist's impact over time: The predictive power of clustering with peers | en |
dc.type | conferenceItem | en |
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