MapReduce Based Personalized Locality Sensitive Hashing for Similarity Joins on Large Scale Data
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 f...
Main Authors: | Wang, Jingjing, Lin, Chen |
---|---|
Format: | Online |
Language: | English |
Published: |
Hindawi Publishing Corporation
2015
|
Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4431368/ |
Similar Items
-
Efficient and Scalable Graph Similarity Joins in MapReduce
by: Chen, Yifan, et al.
Published: (2014) -
“At scale” author name matching with Hadoop/MapReduce
by: James, Powell, et al.
Published: (2012) -
Meteorological Data Analysis Using MapReduce
by: Fang, Wei, et al.
Published: (2014) -
Fractal MapReduce decomposition of sequence alignment
by: Almeida, Jonas S, et al.
Published: (2012) -
Halvade: scalable sequence analysis with MapReduce
by: Decap, Dries, et al.
Published: (2015)