Proceedings ArticleDOI
On the resemblance and containment of documents
TLDR
The basic idea is to reduce these issues to set intersection problems that can be easily evaluated by a process of random sampling that could be done independently for each document.Abstract:
Given two documents A and B we define two mathematical notions: their resemblance r(A, B) and their containment c(A, B) that seem to capture well the informal notions of "roughly the same" and "roughly contained." The basic idea is to reduce these issues to set intersection problems that can be easily evaluated by a process of random sampling that can be done independently for each document. Furthermore, the resemblance can be evaluated using a fixed size sample for each document. This paper discusses the mathematical properties of these measures and the efficient implementation of the sampling process using Rabin (1981) fingerprints.read more
Citations
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Journal ArticleDOI
Kernelized Locality-Sensitive Hashing
Brian Kulis,Kristen Grauman +1 more
TL;DR: It is shown how to generalize locality-sensitive hashing to accommodate arbitrary kernel functions, making it possible to preserve the algorithm's sublinear time similarity search guarantees for a wide class of useful similarity functions.
Proceedings ArticleDOI
Finding interesting associations without support pruning
Edith Cohen,Mayur Datar,S. Fujiwara,Aristides Gionis,Piotr Indyk,Rajeev Motwani,Jeffrey D. Ullman,Cheng Yang +7 more
TL;DR: This work develops a family of algorithms for solving association rule mining, employing a combination of random sampling and hashing techniques and provides an analysis of the algorithms developed and conduct experiments on real and synthetic data to obtain a comparative performance analysis.
Proceedings ArticleDOI
Min-wise independent permutations (extended abstract)
TL;DR: This research was motivated by the fact that such a family of permutations is essential to the algorithm used in practice by the AltaVista web index software to detect and filter near-duplicate documents.
Proceedings Article
Redundancy elimination within large collections of files
TL;DR: The scheme, called Redundancy Elimination at the Block Level (REBL), leverages the benefits of compression, duplicate block suppression, and delta-encoding to eliminate a broad spectrum of redundant data in a scalable and efficient manner.
Proceedings ArticleDOI
TreeJuxtaposer: scalable tree comparison using Focus+Context with guaranteed visibility
TL;DR: The idea of "guaranteed visibility", where highlighted areas are treated as landmarks that must remain visually apparent at all times, is introduced in TreeJuxtaposer, a system designed to support the comparison task for large trees of several hundred thousand nodes.
References
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Book
The Probabilistic Method
TL;DR: A particular set of problems - all dealing with “good” colorings of an underlying set of points relative to a given family of sets - is explored.
Journal ArticleDOI
Syntactic clustering of the Web
TL;DR: An efficient way to determine the syntactic similarity of files is developed and applied to every document on the World Wide Web, and a clustering of all the documents that are syntactically similar is built.
Journal ArticleDOI
Min-Wise Independent Permutations
TL;DR: This research was motivated by the fact that such a family of permutations is essential to the algorithm used in practice by the AltaVista web index software to detect and filter near-duplicate documents.
Proceedings Article
Finding similar files in a large file system
TL;DR: Application of sif can be found in file management, information collecting, program reuse, file synchronization, data compression, and maybe even plagiarism detection.
Proceedings ArticleDOI
Copy detection mechanisms for digital documents
TL;DR: This paper proposes a system for registering documents and then detecting copies, either complete copies or partial copies, and describes algorithms for such detection, and metrics required for evaluating detection mechanisms (covering accuracy, efficiency, and security).