Near-duplicate documents can adversely affect the efficiency and effectiveness of search engines. Due to the pairwise nature of the comparisons required for near-duplicate detection, this process is extremely costly in terms of the time and processing power it requires. Despite the ubiquitous presence of near-duplicate detection algorithms in commercial search engines, their application and impact in research environments is not fully explored. The implementation of near-duplicate detection algorithms forces trade-offs between efficiency and effectiveness, entailing careful testing and measurement to ensure acceptable performance. In this thesis, we describe and evaluate a scalable implementation of a near-duplicate detection algorithm, based on standard shingling techniques, running under a MapReduce framework. We explore two different shingle sampling techniques and analyze their impact on the near-duplicate document detection process. In addition, we investigate the prevalence of near-duplicate documents in the runs submitted to the adhoc task of TREC 2009 web track.

Near-duplicate documents can adversely affect the efficiency and effectiveness of search engines. Due to the pairwise nature of the comparisons required for near-duplicate detection, this process is extremely costly in terms of the time and processing power it requires. Despite the ubiquitous presence of near-duplicate detection algorithms in commercial search engines, their application and impact in research environments is not fully explored. The implementation of near-duplicate detection algorithms forces trade-offs between efficiency and effectiveness, entailing careful testing and measurement to ensure acceptable performance. In this thesis, we describe and evaluate a scalable implementation of a near-duplicate detection algorithm, based on standard shingling techniques, running under a MapReduce framework. We explore two different shingle sampling techniques and analyze their impact on the near-duplicate document detection process. In addition, we investigate the prevalence of near-duplicate documents in the runs submitted to the adhoc task of TREC 2009 web track.

No description available.

No description available.

For the task of near-duplicate document detection, comparison approaches based on bag-of-words used in information retrieval community are not sufficiently accurate. This work presents novel approach when instance-level constraints are given for documents and it is needed to retrieve them, given new query document for near-duplicate detection. The framework incorporates instance-level constraints and clusters documents into groups using novel clustering approach Grouped Latent Dirichlet Allocation (gLDA). Then distance metric is learned for each cluster using large margin nearest neighbor algorithm and finally ranked documents for given new unknown document using learnt distance metrics. The variety of experimental results on various datasets demonstrate that our clustering method (gLDA with side constraints) performs better than other clustering methods and the overall approach outperforms other near-duplicate detection algorithms.

Finding near-duplicate documents is an interesting problem but the existing methods are not suitable for large scale datasets and memory constrained systems. In this work, we developed approaches that tackle the problem of finding near-duplicates while improving query performance and using less memory. We then carried out an evaluation of our method on a dataset of 70M web documents, and showed that our method works really well. The results indicated that our method could achieve a reduction in space by a factor of 5 while improving the query time by a factor of 4 with a recall of 0.95 for finding all near-duplicates when the dataset is in memory. With the same recall and same reduction in space, we could achieve an improvement in query-time by a factor of 4.5 while finding first the near-duplicate for an in memory dataset. When the dataset was stored on a disk, we could achieve an improvement in performance by 7 times for finding all near-duplicates and by 14 times when finding the first near-duplicate.

From the large scale of documents effective to find the near-duplicate document, has been a very important issue. In this paper, we propose a new method to detect near-duplicate document from the large scale dataset, our method is divided into three parts, feature selection, similarity measure and discriminant derivation. In feature selection, document will be detected after preprocessed. Documents have to remove signals, stop words ... and so on. We measure the value of the term weight in the sentence, and then choose the terms which have higher weight in the sentence. These terms collected as a feature of the document. The document¡¦s feature set collected by these features. Similarity measure is based on similarity function to measure the similarity value between two feature sets. Discriminant derivation is based on support vector machine which train a classifiers to identify whether a document is a near-duplicate or not. support vector machine is a supervised learning strategy. It trains a classifier by the training patterns. In the characteristics of documents, the sentence-level features are more effective than terms-level features. Besides, learning a discriminant by SVM can avoid trial-and-error efforts required in conventional methods. Trial-and-error is going to find a threshold, a discriminant value to define document¡¦s relation. In the final analysis of experiment, our method is effective in near-duplicate document detection than other methods.