Relevant bibliographies by topics / Duplicate Detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Duplicate Detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Duplicate Detection"

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Sharapova, Ekaterina V., and Ruslan V. Sharapov. "System of Fuzzy Duplicates Detection." Applied Mechanics and Materials 490-491 (January 2014): 1503–7. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1503.

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In the paper we discuss the problem of fuzzy duplicate detecting. There are given the basic approaches to detection of text duplicates. We review the existing methods of fuzzy duplicate detecting. There is presented algorithm of fuzzy duplicate detection. Algorithm is based on method of shingles. We describe modification of algorithm. We propose to consider not all text of document but its processed and filtered copy. There is presented the structure of system for fuzzy duplicates detection. System checks text duplications in the internal database and in Internet.
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Dr, . S. . Thavamani. "Hybrid Method for Detecting Duplicate Image by Using Image Retrieval Technique in Data Mining." International Journal of Mathematics And Computer Research 09, no. 04 (2021): 2225–30. https://doi.org/10.47191/ijmcr/v9i4.02.

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Duplicated images cause several problems in online sites, so these demand special attention. To address the disadvantages of frames copy detection, the Hybrid Method of Detecting Duplicate Image by Using Image Retrieval Technique in Data Mining was propose d. We use the new method of eliminating duplicates in this example. To address the disadvantages of frames copy detection, the Hybrid Method of Detecting Duplicate Image by Using Image Retrieval Technique in Data Mining was proposed. The new method of eli minating duplicates in this example has proposed. Using this method, you can get rid of
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S., Divya. "A Novel Approach for Progressive Duplicate Detection for Quality Assurance." International Journal of Trend in Scientific Research and Development 2, no. 4 (2018): 179–82. https://doi.org/10.31142/ijtsrd12972.

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In reality the data set may have at least one portrayal of a similar certifiable elements. Duplicate may emerge because of exchange errors and because of deficient information. Expelling such duplicate, all things considered, is a perplexing errand. It isnt direct to productively discover and expel the duplicates from a vast data set. This paper center around correlation with conventional duplicate discovery strategies Incremental Sorted Neighborhood Method ISNM and the Duplicate Count Strategy DCS technique with Progressive Sorted Neighborhood Method PSNM technique. S. Divya "A Novel App
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Ramesh Shahabadkar, Dr, Dr Ramesh Shahabadkar1*, Dr S Sai Satyanarayana Reddy, and P. Devika. "A Method For Detecting Duplicate And Near-Duplicate Images Penetration." International Journal of Engineering & Technology 7, no. 4.6 (2018): 96. http://dx.doi.org/10.14419/ijet.v7i4.6.20438.

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A method for detection of duplicate or near-duplicate image penetration from images in the similar group by distribution of color and other attributes of the image. Distinctive sceneries of the images penetration are identified. Each couple of images penetration with at least one distictive scenary is mutual; the distictive scenary of each image penetration is allied to normalize whether the couple is duplicates or near-duplicates.
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P, Veeramuthu. "Analysis of Progressive Duplicate Data Detection." Journal of Computational Mathematica 3, no. 2 (2019): 41–50. http://dx.doi.org/10.26524/cm53.

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Papenbrock, Thorsten, Arvid Heise, and Felix Naumann. "Progressive Duplicate Detection." IEEE Transactions on Knowledge and Data Engineering 27, no. 5 (2015): 1316–29. http://dx.doi.org/10.1109/tkde.2014.2359666.

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Subasic, Ilija, Nebojsa Gvozdenovic, and Kris Jack. "De-duplicating a large crowd-sourced catalogue of bibliographic records." Program 50, no. 2 (2016): 138–56. http://dx.doi.org/10.1108/prog-02-2015-0021.

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Purpose – The purpose of this paper is to describe a large-scale algorithm for generating a catalogue of scientific publication records (citations) from a crowd-sourced data, demonstrate how to learn an optimal combination of distance metrics for duplicate detection and introduce a parallel duplicate clustering algorithm. Design/methodology/approach – The authors developed the algorithm and compared it with state-of-the art systems tackling the same problem. The authors used benchmark data sets (3k data points) to test the effectiveness of our algorithm and a real-life data ( > 90 million)
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Gosavi, Pramod. B., and M.A. Patel. "Duplicate Detection in Hierarchical XML Multimedia Data Using Improved Multidup Method." COMPUSOFT: An International Journal of Advanced Computer Technology 04, no. 06 (2015): 1871–75. https://doi.org/10.5281/zenodo.14786367.

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Today"s important task is to clean data in data warehouses which has complex hierarchical structure. This is possibly done by detecting duplications in large databases to increase the efficiency of data mining and to make data mining effective. Recently new algorithms are proposed that consider relations in a single table; hence by comparing records pairwise they can easily find out duplications. But now a day the data is being stored in more complex and semistructured or hierarchical structure and the problem arose is how to detect duplicates on this XML data. Due to differences between vario
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Bamnote, Dr G. R., and Ms Deepti Ingole. "Design of Efficient Model to Predict Duplications in Questionnaire Forum using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 5893–97. http://dx.doi.org/10.22214/ijraset.2023.53088.

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Abstract: Detection of duplicate sentences from a corpus containing a pair of sentences deals with identifying whether two sentences in the pair convey the same meaning or not. This detection of duplicates helps in deduplication, a process in which duplicates are removed. Traditional natural language processing techniques are less accurate in identifying similarity between sentences, such similar sentences can also be referred as paraphrases. Using Quora and Twitter paraphrase corpus, we explored various approaches including several machine learning algorithms to obtain a liable approach that
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Kumar. "CLUSTER BASED DUPLICATE DETECTION." Journal of Computer Science 9, no. 11 (2013): 1514–18. http://dx.doi.org/10.3844/jcssp.2013.1514.1518.

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Dissertations / Theses on the topic "Duplicate Detection"

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Draisbach, Uwe, Felix Naumann, Sascha Szott, and Oliver Wonneberg. "Adaptive windows for duplicate detection." Universität Potsdam, 2012. http://opus.kobv.de/ubp/volltexte/2012/5300/.

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Duplicate detection is the task of identifying all groups of records within a data set that represent the same real-world entity, respectively. This task is difficult, because (i) representations might differ slightly, so some similarity measure must be defined to compare pairs of records and (ii) data sets might have a high volume making a pair-wise comparison of all records infeasible. To tackle the second problem, many algorithms have been suggested that partition the data set and compare all record pairs only within each partition. One well-known such approach is the Sorted Neighborhood Me
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Weis, Melanie. "Duplicate detection in XML data." Duisburg Köln WiKu, 2007. http://d-nb.info/987676849/04.

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Huang, Yuzhou. "Duplicate detection in XML Web data /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20HUANG.

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Matsakis, Nicholas E. (Nicholas Elias) 1976. "Active duplicate detection with Bayesian nonparametric models." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/57679.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 129-137).<br>When multiple databases are merged, an essential step is identifying sets of records that refer to the same entity. Called duplicate detection, this task is typically tedious to perform manually, and so a variety of automated methods have been developed for partitioning a collection of records into coreference sets. This task is complicated by ambiguous or noisy field values, so syste
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Norckauer, Heber Regal. "Duplicate entry detection in mailing and participation lists." Thesis, Massachusetts Institute of Technology, 1990. http://hdl.handle.net/1721.1/61059.

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Seshasai, Shreyes. "Efficient near duplicate document detection for specialized corpora." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53116.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.<br>Includes bibliographical references (p. 75-77).<br>Knowledge of near duplicate documents can be adventagous to search engines, even those that only cover a small enterprise or specialized corpus. In this thesis, we investigate improvements to simhash, a signature-based method which can be used to efficiently detect near duplicate documents. We implement simhash in its original form, and demonstrate its effectiveness on a small corpus of newspaper articles, and improve its acc
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Lund, Max. "Duplicate Detection and Text Classification on Simplified Technical English." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158714.

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This thesis investigates the most effective way of performing classification of text labels and clustering of duplicate texts in technical documentation written in Simplified Technical English. Pre-trained language models from transformers (BERT) were tested against traditional methods such as tf-idf with cosine similarity (kNN) and SVMs on the classification task. For detecting duplicate texts, vector representations from pre-trained transformer and LSTM models were tested against tf-idf using the density-based clustering algorithms DBSCAN and HDBSCAN. The results show that traditional method
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Leland, Robert. "Duplicate Detection with PMC -- A Parallel Approach to Pattern Matching." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9642.

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<p>Fuzzy duplicate detection is an integral part of data cleansing. It consists of finding a set of duplicate records, correctly identifying the original or most representative record and removing the rest. The rate of Internet usage, and data availability and collectability is increasing so we get more and more access to data. A lot of this data is collected from, and entered by humans and this causes noise in the data from typing mistakes, spelling discrepancies, varying schemas, abbreviations, and more. Because of this data cleansing and approximate duplicate detection is now more important
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Herschel, Melanie, and Felix Naumann. "Space and time scalability of duplicate detection in graph data." Universität Potsdam, 2008. http://opus.kobv.de/ubp/volltexte/2009/3285/.

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Duplicate detection consists in determining different representations of real-world objects in a database. Recent research has considered the use of relationships among object representations to improve duplicate detection. In the general case where relationships form a graph, research has mainly focused on duplicate detection quality/effectiveness. Scalability has been neglected so far, even though it is crucial for large real-world duplicate detection tasks. In this paper we scale up duplicate detection in graph data (DDG) to large amounts of data and pairwise comparisons, using the support
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Anderson, Jon-Paul. "Duplicate with Choose: Using Statistics for Fault Mitigation." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/5964.

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This dissertation presents a novel technique called duplicate with choose (DWCh) which is a modification of the fault detection technique duplicate with compare (DWC). DWCh adds a smart decider block to DWC that monitors the duplicated circuits and decides which circuit is fault free when a fault occurs. If chosen correctly, DWCh is able to mask faults at a lower cost than conventional techniques like TMR.This dissertation derives reliability expressions for DWCh showing that under ideal conditions its reliability exceeds the most commonly used fault masking technique for spacecraft, triple mo
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Books on the topic "Duplicate Detection"

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Weis, M. Relationship-based duplicate detection. Institut für Informatik, Humboldt-Universität zu Berlin, 2006.

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Naumann, Felix, and Melanie Herschel. An Introduction to Duplicate Detection. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01835-0.

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Melanie, Herschel, ed. An introduction to duplicate detection. Morgan & Claypool, 2010.

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service), SpringerLink (Online, ed. Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer Berlin Heidelberg, 2012.

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Heyer, Georgette. Duplicate death. Chivers Press, 1993.

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Heyer, Georgette. Duplicate Death. Sourcebooks, Inc., 2010.

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Heyer, Georgette. Duplicate death. Arlington, 1990.

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Heyer, Georgette. Duplicate death. Sourcebooks Landmark, 2010.

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Heyer, Georgette. Duplicate death. Sourcebooks Landmark, 2010.

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Wolzien, Valerie. Death in duplicate: A novel. Ballantine Books, Fawcett, 2005.

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Book chapters on the topic "Duplicate Detection"

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Webb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, et al. "Duplicate Detection." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_233.

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Naumann, Felix, and Melanie Herschel. "Duplicate Detection Algorithms." In An Introduction to Duplicate Detection. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01835-0_4.

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Deng, Ke, Liwei Wang, Xiaofang Zhou, Shazia Sadiq, and Gabriel Pui Cheong Fung. "Active Duplicate Detection." In Database Systems for Advanced Applications. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12026-8_43.

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Lehti, Patrick, and Peter Fankhauser. "Unsupervised Duplicate Detection Using Sample Non-duplicates." In Journal on Data Semantics VII. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11890591_5.

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Satoh, Shin’ichi. "Video Near-duplicate Detection." In Encyclopedia of Multimedia. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-78414-4_73.

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Lehti, Patrick, and Peter Fankhauser. "Probabilistic Iterative Duplicate Detection." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11575801_19.

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Naumann, Felix, and Melanie Herschel. "Conclusion and Outlook." In An Introduction to Duplicate Detection. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01835-0_6.

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Naumann, Felix, and Melanie Herschel. "Similarity Functions." In An Introduction to Duplicate Detection. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01835-0_3.

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Alonso, Omar, Dennis Fetterly, and Mark Manasse. "Duplicate News Story Detection Revisited." In Information Retrieval Technology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45068-6_18.

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Holte, Robert C. "Move Pruning and Duplicate Detection." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38457-8_4.

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Conference papers on the topic "Duplicate Detection"

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Zia, Saad, and Muhammad Sarim Nadeem. "Efficient Duplicate Question Detection." In 2024 18th International Conference on Open Source Systems and Technologies (ICOSST). IEEE, 2024. https://doi.org/10.1109/icosst64562.2024.10871150.

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Yang, Chunlei, Jinye Peng, Xiaoyi Feng, and Jianping Fan. "Speed up duplicate/near-duplicate image detection." In the Second International Conference. ACM Press, 2010. http://dx.doi.org/10.1145/1937728.1937751.

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Li, Zhen, Chen Lin, and Bi-cheng Li. "Duplicate Reviews Detection." In International Conference on Biomedical Engineering and Computer Science (ICBECS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icbecs.2010.5462334.

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Spitz, Larry. "Duplicate document detection." In Electronic Imaging '97, edited by Luc M. Vincent and Jonathan J. Hull. SPIE, 1997. http://dx.doi.org/10.1117/12.270062.

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Medidar, Nikita, and Manik Chavan. "Data Duplicate Detection." In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2018. http://dx.doi.org/10.1109/icccnt.2018.8494135.

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Vajda, Peter, Ivan Ivanov, Lutz Goldmann, and Touradj Ebrahimi. "Omnidirectional object duplicate detection." In 2011 Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE). IEEE, 2011. http://dx.doi.org/10.1109/dsp-spe.2011.5739235.

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Bressan, Federica, Joren Six, and Marc Leman. "Applications of duplicate detection." In DLfM '17: 4th International Workshop on Digital Libraries for Musicology. ACM, 2017. http://dx.doi.org/10.1145/3144749.3144759.

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Cholakov, Todor, and Dimitar Birov. "Duplicate code detection algorithm." In the 16th International Conference. ACM Press, 2015. http://dx.doi.org/10.1145/2812428.2812449.

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Wang, Qingye, Bowen Xu, Xin Xia, Ting Wang, and Shanping Li. "Duplicate Pull Request Detection." In Internetware '19: The 11th Asia-Pacific Symposium on Internetware. ACM, 2019. http://dx.doi.org/10.1145/3361242.3361254.

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Conrad, Jack G., Xi S. Guo, and Cindy P. Schriber. "Online duplicate document detection." In the twelfth international conference. ACM Press, 2003. http://dx.doi.org/10.1145/956863.956946.

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Reports on the topic "Duplicate Detection"

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Costa, F., X. Pougnard, and H. Li. Duplicate Address Detection Proxy. RFC Editor, 2013. http://dx.doi.org/10.17487/rfc6957.

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Asati, R., H. Singh, W. Beebee, C. Pignataro, E. Dart, and W. George. Enhanced Duplicate Address Detection. RFC Editor, 2015. http://dx.doi.org/10.17487/rfc7527.

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Moore, N. Optimistic Duplicate Address Detection (DAD) for IPv6. RFC Editor, 2006. http://dx.doi.org/10.17487/rfc4429.

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Candia-Puma, Mayron Antonio, Laura Yesenia Machaca-Luque, Brychs Milagros Roque-Pumahuanca, et al. Accuracy of the diagnostic tests for the detection of Chagas disease: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.9.0132.

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Review question / Objective: The objective of the current work is to systematically review and summarize the available literature on the diagnostic accuracy of diagnostic tests for Chagas Disease. Eligibility criteria: The studies were selected in three stages. In the first, non-English language articles, duplicate articles, reviews, and meta-analyses were excluded, only articles published after 1990 and conducted on humans were included. In the second stage, the titles and ab-stracts of the articles selected through the search strategy were examined. Finally, the highly relevant full studies
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Bosch, S. Sieve Email Filtering: Detecting Duplicate Deliveries. RFC Editor, 2014. http://dx.doi.org/10.17487/rfc7352.

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Hovav, Ran, Peggy Ozias-Akins, and Scott A. Jackson. The genetics of pod-filling in peanut under water-limiting conditions. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7597923.bard.

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Pod-filling, an important yield-determining stage is strongly influenced by water stress. This is particularly true for peanut (Arachishypogaea), wherein pods are developed underground and are directly affected by the water condition. Pod-filling in peanut has a significant genetic component as well, since genotypes are considerably varied in their pod-fill (PF) and seed-fill (SF) potential. The goals of this research were to: Examine the effects of genotype, irrigation, and genotype X irrigation on PF and SF. Detect global changes in mRNA and metabolites levels that accompany PF and SF. Explo
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Bercovier, Herve, and Ronald P. Hedrick. Diagnostic, eco-epidemiology and control of KHV, a new viral pathogen of koi and common carp. United States Department of Agriculture, 2007. http://dx.doi.org/10.32747/2007.7695593.bard.

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Original objectives and revisions-The proposed research included these original objectives: field validation of diagnostic tests (PCR), the development and evaluation of new sensitive tools (LC-PCR/TaqManPCR, antibody detection by ELISA) including their use to study the ecology and the epidemiology of KHV (virus distribution in the environment and native cyprinids) and the carrier status of fish exposed experimentally or naturally to KHV (sites of virus replication and potential persistence or latency). In the course of the study we completed the genome sequence of KHV and developed a DNA arra
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