Syntactic clustering of the Web
Selected papers from the sixth international conference on World Wide Web
Collection statistics for fast duplicate document detection
ACM Transactions on Information Systems (TOIS)
Similarity estimation techniques from rounding algorithms
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Finding near-duplicate web pages: a large-scale evaluation of algorithms
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Detecting near-duplicates for web crawling
Proceedings of the 16th international conference on World Wide Web
Document similarity based on concept tree distance
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Partial duplicate detection for large book collections
Proceedings of the 20th ACM international conference on Information and knowledge management
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The detection and potential removal of duplicates is desirable for a number of reasons, such as to reduce the need for unnecessary storage and computation, and to provide users with uncluttered search results. This paper describes an investigation into the application of scalable simhash and shingle state of the art duplicate detection algorithms for detecting near duplicate documents in the CiteSeerX digital library. We empirically explored the duplicate detection methods and evaluated their performance and application to academic documents and identified good parameters for the algorithms. We also analyzed the types of near duplicates identified by each algorithm. The highest F-scores achieved were 0.91 and 0.99 for the simhash and shingle-based methods respectively. The shingle-based method also identified a larger variety of duplicate types than the simhash-based method.