A pruning strategy to improve pairwise comparison-based near-duplicate detection

  • Roya Hassanian-esfahani
  • Mohammad-javad Kargar
Regular Paper


Efficient and accurate near-duplicate detection is a trending topic of research. Complications arise from the great time and space complexities of existing algorithms. This study proposes a novel pruning strategy to improve pairwise comparison-based near-duplicate detection methods. After parsing the documents into punctuation-delimited blocks called chunks, it decides between the categories of “near duplicate,” “non-duplicate” or “suspicious” by applying certain filtering rules. This early decision makes it possible to disregard many of the non-necessary computations—on average 92.95% of them. Then, for the suspicious pairs, common chunks and short chunks are removed and the remaining subsets are reserved for near-duplicate detection. Size of the remaining subsets is on average 4.42% of the original corpus size. Evaluation results show that near-duplicate detection with the proposed strategy in its best configuration (CHT = 8, τ = 0.1) has F-measure = 87.22% (precision = 86.91% and recall = 87.54%). Its F-measure is comparable with the SpotSig method with less execution time. In addition, applying the proposed strategy in a near-duplicate detection process eliminates the need for preprocessing. It is also tunable to achieve the intended levels of near duplication and noise suppression.


Near-duplicate detection Pruning strategy Similarity 



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© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Research Institute for Information and Communications TechnologiesAcademic Center for Education, Culture and ResearchTehranIran
  2. 2.Department of Computer EngineeringUniversity of Science and CultureTehranIran

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