Near-duplicate detection by instance-level constrained clustering

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.