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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 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