Unsupervised record matching with noisy and incomplete data
We consider the problem of duplicate detection in noisy and incomplete data: given a large data set in which each record has multiple entries (attributes), detect which distinct records refer to the same real world entity. This task is complicated by noise (such as misspellings) and missing data, wh...
| Main Authors: | , , , , , |
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| Format: | Article |
| Published: |
Springer
2018
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/51471/ |