document: Efficient similarity joins for near duplicate detection With the increasing amount of data and the need to integrate data from multiple data sources , a challenging issue is to find near duplicate records efficiently . In this paper , we focus on efficient algorithms to find pairs of records such that their similarities are above a given threshold . Several existing algorithms rely on the prefix filtering principle to avoid computing similarity values for all possible pairs of records . We propose new filtering techniques by exploiting the ordering information ; they are integrated into the existing methods and drastically reduce the candidate sizes and hence improve the efficiency . Experimental results show that our proposed algorithms can achieve up to 2.6 x - 5x speed-up over previous algorithms on several real datasets and provide alternative solutions to the near duplicate Web page detection problem .
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