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• W2000292914 abstract "Locality Sensitive Hashing (LSH) has been proposed as an efficient technique for similarity joins for high dimensional data. The efficiency and approximation rate of LSH depend on the number of generated false positive instances and false negative instances. In many domains, reducing the number of false positives is crucial. Furthermore, in some application scenarios, balancing false positives and false negatives is favored. To address these problems, in this paper we propose Personalized Locality Sensitive Hashing (PLSH), where a new banding scheme is embedded to tailor the number of false positives, false negatives, and the sum of both. PLSH is implemented in parallel using MapReduce framework to deal with similarity joins on large scale data. Experimental studies on real and simulated data verify the efficiency and effectiveness of our proposed PLSH technique, compared with state-of-the-art methods." @default.
• W2000292914 created "2016-06-24" @default.
• W2000292914 creator A5052736939 @default.
• W2000292914 creator A5067221645 @default.
• W2000292914 date "2015-01-01" @default.
• W2000292914 modified "2023-10-16" @default.
• W2000292914 title "MapReduce Based Personalized Locality Sensitive Hashing for Similarity Joins on Large Scale Data" @default.
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• W2000292914 doi "https://doi.org/10.1155/2015/217216" @default.
• W2000292914 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4431368" @default.
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