Improving minhashing: De Bruijn sequences and primitive roots for counting trailing zeroes Why things you didn t think you cared about are actually practical means ...
Best case we are left with at most 5 matching elements beyond the elements in the sketch ... list per q-gram in D and compute the minhash sketch of each list: ...
Mining Massive Datasets Wu-Jun Li Department of Computer Science and Engineering Shanghai Jiao Tong University Lecture 10: Finding Similar Items * * Implementation ...
Title: CS206 --- Electronic Commerce Author: Jeff Ullman Last modified by: Jeffrey D. Ullman Created Date: 3/23/2002 8:14:09 PM Document presentation format
Applications of LSH (Locality-Sensitive Hashing) Entity Resolution Fingerprints Similar News Articles Desiderata Whatever form we use for LSH, we want : The time ...
Randomly permute the rows. h(x): first row (in permuted data) in which column x has an 1 ... The probability (over all permutations of rows) that h(x)=h(y) is ...
Data Mining of Very Large Data Frequent itemsets, market baskets A-priori algorithm Hash-based improvements One- or two-pass approximations High-correlation mining
CS276A Text Information Retrieval, Mining, and Exploitation Supplemental Min-wise Hashing Slides [Brod97,Brod98] (Adapted from Rajeev Motwani s CS361A s)
Documents that have lots of shingles in common have similar text, even if the ... Careful: you must pick k large enough, or most documents will have most shingles. ...
If people tend to buy A and B together, then a buyer of A is a good target for ... Example: Few customers buy Handel's Watermusick, but of those who do, 20% buy ...
Represent a customer, e.g., of Netflix, by the set of movies they rented. ... of the same genre are typically rented by similar sets of Netflix customers. ...
What is a Sketch. An approximate representation of the string ... Clustering - Sepia. Partition strings using clustering: Enables pruning of whole clusters ...
Finding Near Duplicates (Adapted from s and material from Rajeev Motwani and Jeff Ullman) ... View sets as columns of a matrix; one row for each element in ...
Simplest question: find sets of items that appear 'frequently' in the baskets. Support for itemset I = the number of baskets containing all items in I. ...
Entity resolution : merging records that refer to the same entity (e.g. ... Postings (Craig's list, B2B Web sites, del.icio.us, social networks, etc. etc.) 10 ...
Support sup(X) = number of baskets with itemset X. Frequent Itemset Problem ... baskets = documents containing sentences. frequent sentence-groups = possible ...
CS276B Text Retrieval and Mining Winter 2005 Lecture 9 Plan for today Web size estimation Mirror/duplication detection Pagerank Size of the web What is the size of ...
Emerging DSMS variety of modern applications. Network monitoring and traffic engineering ... Possibly in adaptive/randomized fashion. Theorem: For any , E ...
... Dublin core metadata in 0.3% Sec. 19.5 Advantages & disadvantages Advantages Clean statistics Independent of crawling ... The Web document ... Hidden text with ...
CS276 Information Retrieval and Web Search Pandu Nayak and Prabhakar Raghavan Lecture 15: Web search basics Random IP addresses Generate random IP addresses Find a ...