'Jaccard similarity' diaporamas de présentation

Finding Approximately Repeated Data

Finding Approximately Repeated Data. Part I. The two most common similarity search variants are: K nearest neighbor search . Find me the five closest Starbucks to my office. Range search. Find me all Starbucks within 4 miles of my office. New variant

926 views • 92 slides

Finding Approximately Repeated Data

Finding Approximately Repeated Data. Part I. Let us review the matrix view of the world. Many datasets naturally are , or can be converted into, sparse matrices. Examples: The rows are patients, the columns are the drugs they have taken.

893 views • 89 slides

Finding Similar Items

1.24k views • 72 slides

Near-Neighbor Search

Near-Neighbor Search. Applications Matrix Formulation Minhashing. Example Application : Face Recognition. We have a database of (say) 1 million face images. We want to find the most similar images in the database.

448 views • 43 slides

Ryan O'Donnell (CMU) Yi Wu (CMU, IBM) Yuan Zhou (CMU)

Optimal lower bounds for Locality Sensitive Hashing. (except when q is tiny). Ryan O'Donnell (CMU) Yi Wu (CMU, IBM) Yuan Zhou (CMU). Locality Sensitive Hashing [Indyk-Motwani '98]. h :. objects. sketches. H : family of hash functions h s.t.

459 views • 31 slides

Ryan O'Donnell (CMU) Yi Wu (CMU, IBM) Yuan Zhou (CMU)

Optimal lower bounds for Locality Sensitive Hashing. (except when q is tiny). Ryan O'Donnell (CMU) Yi Wu (CMU, IBM) Yuan Zhou (CMU). Locality Sensitive Hashing [Indyk-Motwani '98]. h :. objects. sketches. H : family of hash functions h s.t.

429 views • 31 slides

Ryan O'Donnell (CMU) Yi Wu (CMU, IBM) Yuan Zhou (CMU)

Optimal lower bounds for Locality Sensitive Hashing. (except when q is tiny). Ryan O'Donnell (CMU) Yi Wu (CMU, IBM) Yuan Zhou (CMU). Locality Sensitive Hashing [Indyk-Motwani '98]. h :. objects. sketches. H : family of hash functions h s.t.

469 views • 26 slides

Crawling and Aligning Scholarly Presentations and Documents from the Web

Crawling and Aligning Scholarly Presentations and Documents from the Web. By SARAVANAN.S 09/09/2011 Under the guidance of A/P Min-Yen Kan. Motivation. More articles ? more users Searching for documents is difficult Aim: Find pairs of presentations and documents automatically.

217 views • 14 slides

Near-duplicates detection

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.

289 views • 6 slides