# Near-duplicates detection - PowerPoint PPT Presentation

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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.

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Near-duplicates detection

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#### Presentation Transcript

Comparison of the two algorithms seen in class

Romain Colle

### 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

• 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.

### Conclusion

• Algorithm SK rocks !

• However, it is computationally more expensive

• Tradeoff between speed and recall/precision

(given that algorithm SH performs quite well)