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• W3008706840 abstract "Abstract Similarity join is a key operation in metric databases. It retrieves all pairs of elements that are similar. Solving such a problem usually requires comparing every pair of objects of the datasets, even when indexing and ad hoc algorithms are used. We propose a simple and efficient algorithm for the computation of the approximated k nearest neighbor self-similarity join. This algorithm computes Θ ( n 3 ∕ 2 ) distances and it is empirically shown that it reaches an empirical precision of 46% in real-world datasets. We provide a comparison to other common techniques such as Quickjoin and Locality-Sensitive Hashing and argue that our proposal has a better execution time and average precision." @default.
• W3008706840 created "2020-03-06" @default.
• W3008706840 creator A5009309241 @default.
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• W3008706840 date "2020-07-01" @default.
• W3008706840 modified "2023-09-23" @default.
• W3008706840 title "An efficient algorithm for approximated self-similarity joins in metric spaces" @default.
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• W3008706840 doi "https://doi.org/10.1016/j.is.2020.101510" @default.
• W3008706840 hasPublicationYear "2020" @default.
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