%0 Conference Paper %1 citeulike:2295267 %A Yang, Hui %A Callan, Jamie %B Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval %C New York, NY, USA %D 2006 %I ACM %K clustering %P 421--428 %R 10.1145/1148170.1148243 %T Near-duplicate Detection by Instance-level Constrained Clustering %U http://dx.doi.org/10.1145/1148170.1148243 %X For the task of near-duplicated document detection, both traditional fingerprinting techniques used in database community and bag-of-word comparison approaches used in information retrieval community are not sufficiently accurate. This is due to the fact that the characteristics of near-duplicated documents are different from that of both älmost-identical" documents in the data cleaning task and "relevant" documents in the search task. This paper presents an instance-level constrained clustering approach for near-duplicate detection. The framework incorporates information such as document attributes and content structure into the clustering process to form near-duplicate clusters. Gathered from several collections of public comments sent to U.S. government agencies on proposed new regulations, the experimental results demonstrate that our approach outperforms other near-duplicate detection algorithms and as about as effective as human assessors. %@ 1-59593-369-7

@inproceedings{citeulike:2295267, abstract = {{For the task of near-duplicated document detection, both traditional fingerprinting techniques used in database community and bag-of-word comparison approaches used in information retrieval community are not sufficiently accurate. This is due to the fact that the characteristics of near-duplicated documents are different from that of both "almost-identical" documents in the data cleaning task and "relevant" documents in the search task. This paper presents an instance-level constrained clustering approach for near-duplicate detection. The framework incorporates information such as document attributes and content structure into the clustering process to form near-duplicate clusters. Gathered from several collections of public comments sent to U.S. government agencies on proposed new regulations, the experimental results demonstrate that our approach outperforms other near-duplicate detection algorithms and as about as effective as human assessors.}}, added-at = {2018-03-19T12:24:51.000+0100}, address = {New York, NY, USA}, author = {Yang, Hui and Callan, Jamie}, biburl = {https://www.bibsonomy.org/bibtex/2f447fc375c8675f5dc51f570ef52bddc/aho}, booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval}, citeulike-article-id = {2295267}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1148243}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1148170.1148243}, doi = {10.1145/1148170.1148243}, interhash = {0703044e3abd1580680e66f2355813c6}, intrahash = {f447fc375c8675f5dc51f570ef52bddc}, isbn = {1-59593-369-7}, keywords = {clustering}, location = {Seattle, Washington, USA}, pages = {421--428}, posted-at = {2011-01-11 17:15:07}, priority = {2}, publisher = {ACM}, series = {SIGIR '06}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{Near-duplicate Detection by Instance-level Constrained Clustering}}, url = {http://dx.doi.org/10.1145/1148170.1148243}, year = 2006 }