| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Feelders, Ad | |
| dc.contributor.author | Addi, Mohamed | |
| dc.date.accessioned | 2022-04-20T23:00:35Z | |
| dc.date.available | 2022-04-20T23:00:35Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/41508 | |
| dc.description.abstract | The application of heuristic weighted cluster-editing algorithms within the scope
of entity deduplication is a relatively unexplored area. This research has aimed
at comparing the efficacy of different heuristics on both real-world and artificiallygenerated entity-deduplication data-sets. The research has shown that the
Force, Spectral, Vote/BOEM, and Split-Merge heuristics perform relatively well
for precision in comparison to the benchmark heuristics Pivot and Closure on
a variety of data-sets | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | The application of heuristic weighted cluster-editing algorithms within the scope
of entity deduplication is a relatively unexplored area. This research has aimed
at comparing the efficacy of different heuristics on both real-world and artificiallygenerated entity-deduplication data-sets. The research has shown that the
Force, Spectral, Vote/BOEM, and Split-Merge heuristics perform relatively well
for precision in comparison to the benchmark heuristics Pivot and Closure on
a variety of data-sets | |
| dc.title | Experimental comparison of heuristic
cluster-editing algorithms for entity
deduplication. | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.keywords | Entit-Deduplication, Heuristic weighted cluster-editing, machine-learning, correlation-clustering | |
| dc.subject.courseuu | Computing Science | |
| dc.thesis.id | 3463 | |
