Comparison of the two algorithms seen in class romain colle
This presentation is the property of its rightful owner.
Sponsored Links
1 / 6

Near-duplicates detection PowerPoint PPT Presentation


  • 174 Views
  • Uploaded on
  • Presentation posted in: General

Comparison of the two algorithms seen in class Romain Colle. Near-duplicates detection. Description of algorithms. 1 st pass through the data : Both algorithms compute a signature for each document, and perform LSH on these signatures.

Download Presentation

Near-duplicates detection

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Comparison of the two algorithms seen in class romain colle

Comparison of the two algorithms seen in class

Romain Colle

Near-duplicates detection


Description of algorithms

Description of algorithms

  • 1st pass through the data : Both algorithms compute a signature for each document, and perform LSH on these signatures.

  • 2nd pass through the data : Verification of the relevance of the duplicates pairs found (Jaccard similarity).

  • Algorithm SH uses Shingles + MinHashing to compute the signatures.

  • Algorithm SK uses sketches of projections on random hyperplanes to compute the signatures.


Experimentation method

Experimentation method

  • Run both algorithms on the data set (WebBase), and compute precision.

  • Remove duplicates pairs found from the data set.

  • Generate and insert large amounts of (near-) duplicates documents (~10% of the data set).

  • Run both algorithms on the new dataset, and compute precision and recall.


Results original data set

Results (original data set)


Results modified dataset

Results (modified dataset)


Conclusion

Conclusion

  • Algorithm SK rocks !

  • However, it is computationally more expensive

  • Tradeoff between speed and recall/precision

    (given that algorithm SH performs quite well)


  • Login