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Top-k Set Similarity Joins
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BibTeX
@MISC{Xiao_top-kset,
author = {Chuan Xiao and Wei Wang and Xuemin Lin and Haichuan Shang},
title = {Top-k Set Similarity Joins},
year = {}
}
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Abstract
Abstract — Similarity join is a useful primitive operation underlying many applications, such as near duplicate Web page detection, data integration, and pattern recognition. Traditional similarity joins require a user to specify a similarity threshold. In this paper, we study a variant of the similarity join, termed top-k set similarity join. It returns the top-k pairs of records ranked by their similarities, thus eliminating the guess work users have to perform when the similarity threshold is unknown before hand. An algorithm, topk-join, is proposed to answer top-k similarity join efficiently. It is based on the prefix filtering principle and employs tight upper bounding of similarity values of unseen pairs. Experimental results demonstrate the efficiency of the proposed algorithm on large-scale real datasets. I.
Keyphrases
top-k set similarity join similarity threshold near duplicate web page detection abstract similarity join traditional similarity guess work user many application similarity join large-scale real datasets unseen pair pattern recognition similarity value top-k pair useful primitive operation experimental result top-k similarity join tight upper bounding data integration
