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Scalable all-pairs similarity search in metric spaces
2013
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '13
Given a set of entities, the all-pairs similarity search aims at identifying all pairs of entities that have similarity greater than (or distance smaller than) some user-defined threshold. In this article, we propose a parallel framework for solving this problem in metric spaces. Novel elements of our solution include: i) flexible support for multiple metrics of interest; ii) an autonomic approach to partition the input dataset with minimal redundancy to achieve good load-balance in the
doi:10.1145/2487575.2487625
dblp:conf/kdd/WangMP13
fatcat:5vtxulyq2rhf7joiossq46beu4