Matches in UGent Biblio for { <https://biblio.ugent.be/publication/791909#aggregation> ?p ?o. }
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- aggregation classification "C1".
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2009".
- aggregation format "application/pdf".
- aggregation hasFormat 791909.bibtex.
- aggregation hasFormat 791909.csv.
- aggregation hasFormat 791909.dc.
- aggregation hasFormat 791909.didl.
- aggregation hasFormat 791909.doc.
- aggregation hasFormat 791909.json.
- aggregation hasFormat 791909.mets.
- aggregation hasFormat 791909.mods.
- aggregation hasFormat 791909.rdf.
- aggregation hasFormat 791909.ris.
- aggregation hasFormat 791909.txt.
- aggregation hasFormat 791909.xls.
- aggregation hasFormat 791909.yaml.
- aggregation isPartOf urn:isbn:9781605584874.
- aggregation language "eng".
- aggregation publisher "Association for Computing Machinery (ACM)".
- aggregation rights "I have retained and own the full copyright for this publication".
- aggregation subject "Science General".
- aggregation title "Fuzzy ants clustering for web people search".
- aggregation abstract "A search engine query for a person's name often brings up web pages corresponding to several people who share the same name. The Web People Search (WePS) problem involves organizing such search results for an ambiguous name query in meaningful clusters, that group together all web pages corresponding to one single individual. A particularly challenging aspect of this task is that it is in general not known beforehand how many clusters to expect. In this paper we therefore propose the use of a Fuzzy Ants clustering algorithm that does not rely on prior knowledge of the number of clusters that need to be found in the data. An evaluation on benchmark data sets from SemEval's WePS1 and WePS2 competitions shows that the resulting system is competitive with the agglomerative clustering Agnes algorithm. This is particularly interesting as the latter involves manual setting of a similarity threshold (or estimating the number of clusters in advance) while the former does not.".
- aggregation authorList BK178558.
- aggregation aggregates 791912.
- aggregation isDescribedBy 791909.
- aggregation similarTo LU-791909.