Identifying and Filtering Near-Duplicate Documents

On the resemblance and containment of documents

Broder, A. (2002) · Compression and Complexity of Sequences (SEQUENCES) · doi:10.1109/sequen.1997.666900 · Cited by 1,818

Syntactic clustering of the Web

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Min-Wise Independent Permutations

Broder, A., Charikar, M., FRIEZE, A. M., & Mitzenmacher, M. (2000) · Journal of Computer and System Sciences · doi:10.1006/jcss.1999.1690 · Cited by 812

Finding similar files in a large file system

Manber, U. (1994) · Cited by 597

Copy detection mechanisms for digital documents

Brin, S., Davis, J., & García-Molina, H. (1995) · Proceedings of the 1995 ACM SIGMOD international conference on Management of data - SIGMOD '95 · doi:10.1145/223784.223855 · Cited by 479

A technique for measuring the relative size and overlap of public Web search engines

Bharat, K. A., & Broder, A. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00127-5 · Cited by 392

Min-wise independent permutations (extended abstract)

Broder, A., Charikar, M., FRIEZE, A. M., & Mitzenmacher, M. (1998) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/276698.276781 · Cited by 364

Untitled work

(1995) · Cited by 296

Some applications of Rabin’s fingerprinting method

Broder, A. (1993) · Springer eBooks · doi:10.1007/978-1-4613-9323-8_11 · Cited by 204

Building a scalable and accurate copy detection mechanism

Shivakumar, N., & García-Molina, H. (1996) · ACM international conference on Digital libraries · doi:10.1145/226931.226961 · Cited by 161

Feature map hashing

Avrithis, Y., Tolias, G., & Kalantidis, Y. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1873985 · Cited by 22

Automatic retrieval of similar content using search engine query interface

Dasdan, A., D’Alberto, P., Kolay, S., & Drome, C. (2009) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1645953.1646043 · Cited by 21

Bottom-k sketches

Cohen, E., & Kaplan, H. Y. (2007) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/1254882.1254926 · Cited by 21

Towards large-scale geometry indexing by feature selection

Tolias, G., Kalantidis, Y., Avrithis, Y., & Kollias, S. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.12.002 · Cited by 21

Set containment join revisited

Bouros, P., Mamoulis, N., Ge, S., & Terrovitis, M. (2015) · Knowledge and Information Systems · doi:10.1007/s10115-015-0895-7 · Cited by 21

Accurate and efficient general-purpose boilerplate detection for crawled web corpora

Schäfer, R. (2016) · Language Resources and Evaluation · doi:10.1007/s10579-016-9359-2 · Cited by 21

External validity of sentiment mining reports: can current methods identify demographic biases, event biases, and manipulation of reviews?

Wijnhoven, F., & Bloemen, O. (2014) · Decision Support Systems · doi:10.1016/j.dss.2013.12.005 · Cited by 21

MetaCache-GPU: Ultra-Fast Metagenomic Classification

Kobus, R., Müller, A., Jünger, D., Hundt, C., & Schmidt, B. (2021) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3472456.3472460 · Cited by 21

CUCWeb

Boleda, G., Bott, S., Meza, R., Castillo, C., Badía, T., & López, V. (2006) · doi:10.3115/1628297.1628301 · Cited by 20

PRUN : Eliminating Information Redundancy for Large Scale Data Backup System

Won, Y., Kim, R., Ban, J., Hur, J., Oh, S., & Lee, J. (2008) · International Conference on Computational Science and Its Applications · doi:10.1109/iccsa.2008.46 · Cited by 20

High Performance Index Build Algorithms for Intranet Search Engines

da Fontoura, M. F. M. C., Shekita, E. J., Zien, J. Y., Rajagopalan, S., & Neumann, A. (2004) · Elsevier eBooks · doi:10.1016/b978-012088469-8.50101-7 · Cited by 20

Proceedings of the 10th Web as Corpus Workshop

(2016) · doi:10.18653/v1/w16-26 · Cited by 20

New Issues in Near-duplicate Detection

Potthast, M., & Stein, B. (2008) · Studies in classification, data analysis, and knowledge organization · doi:10.1007/978-3-540-78246-9_71 · Cited by 19

Continuous word embeddings for detecting local text reuses at the semantic level

Zhang, Q., Kang, J., Qian, J., & Huang, X. (2014) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2600428.2609597 · Cited by 19

Detecting near-duplicate documents using sentence-level features and supervised learning

Lin, Y.-S., Liao, T.-Y., & Lee, S.-J. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.08.045 · Cited by 19

CARE: context-aware sequencing read error correction

Kallenborn, F., Hildebrandt, A., & Schmidt, B. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa738 · Cited by 19

Reducing replication bandwidth for distributed document databases

Xu, L., Pavlo, A., Sengupta, S., Li, J., & Ganger, G. R. (2015) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/2806777.2806840 · Cited by 19

Efficient jaccard-based diversity analysis of large document collections

Deng, F., Siersdorfer, S., & Zerr, S. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2398445 · Cited by 19

Detecting duplicate web documents using clickthrough data

Radlinski, F., Bennett, P. N., & Yilmaz, E. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining · doi:10.1145/1935826.1935859 · Cited by 19

$ezswap$ : Enhanced Compressed Swap Scheme for Mobile Devices

Kim, J.-S., Kim, C., & Seo, E. (2019) · IEEE Access · doi:10.1109/access.2019.2942362 · Cited by 19

Licoeval: Evaluating LLMs on License Compliance in Code Generation

Xu, W., Gao, K., He, H., & Zhou, M. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00052 · Cited by 39

Tracking Down the Negative Group Creativity Effects with the Help of an Artificial Intelligence-Like Support System

Siemon, D., Eckardt, L., & Robra-Bissantz, S. (2015) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2015.37 · Cited by 18

Frequent Itemset Mining for Clustering Near Duplicate Web Documents

Ignatov, D. I., & Kuznetsov, S. O. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03079-6_15 · Cited by 18

PeerDedupe: Insights into the Peer-Assisted Sampling Deduplication

Xing, Y., Li, Z.-C., & Dai, Y. (2010) · IEEE International Conference on Peer-to-Peer Computing (P2P) · doi:10.1109/p2p.2010.5570004 · Cited by 18

The best trail algorithm for assisted navigation of Web sites

Wheeldon, R., & Mark Levene, A. (2004) · International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD) · doi:10.1109/laweb.2003.1250294 · Cited by 18

Near-duplicate detection for web-forums

Muthmann, K., Barczyński, W. M., Brauer, F., & Löser, A. (2009) · International Database Engineering and Applications Symposium (IDEAS) · doi:10.1145/1620432.1620447 · Cited by 18

imDedup: A Lossless Deduplication Scheme to Eliminate Fine-grained Redundancy among Images

Deng, C., Chen, Q., Zou, X., Xu, E., Tang, B., & Xia, W. (2022) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde53745.2022.00085 · Cited by 17

Resemblance and mergence based indexing for high performance data deduplication

Zhang, P., Huang, P. S., He, X., Wang, H., & Zhou, K. (2017) · Journal of Systems and Software · doi:10.1016/j.jss.2017.02.039 · Cited by 17

A framework for efficient evaluation of the fault tolerance of deduplicated storage systems

Rozier, E. W. D., & Sanders, W. H. (2012) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2012.6263921 · Cited by 16

Media Coverage of Female Candidates’ Traits in the 2020 Democratic Presidential Primary

Cassese, E. C., Conroy, M., Mehta, D., & Nestor, F. V. (2021) · Journal of Women Politics & Policy · doi:10.1080/1554477x.2022.2004846 · Cited by 15

Learning hash codes for efficient content reuse detection

Zhang, Q., Wu, Y. X., Ding, Z., & Huang, X. (2012) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2348283.2348339 · Cited by 15

Optimizing Document Similarity Detection in Persian Information Retrieval

Kashefi, O., -, N. M., & Bidgoli, B. M. (2010) · Journal of Convergence Information Technology · doi:10.4156/jcit.vol5.issue2.11 · Cited by 15

Technologies for Reusing Text from the Web

Potthast, M. (2012) · Common Library Network (Der Gemeinsame Bibliotheksverbund) · doi:10.25643/bauhaus-universitaet.1566 · Cited by 15

Approximately Minwise Independence with Twisted Tabulation

Dahlgaard, S., & Thorup, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-08404-6_12 · Cited by 14

Keyword-based correlated network computation over large social media

Li, J., Chengfei Liu, A., & Islam, M. S. (2014) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2014.6816657 · Cited by 14

Constructing test collections by inferring document relevance via extracted relevant information

Rajput, S., Ekstrand-Abueg, M., Pavlu, V., & Aslam, J. A. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2396783 · Cited by 14

An indexing scheme and descriptor for 3D object retrieval based on local shape querying

van Blokland, B. I., & Theoharis, T. (2020) · Computers & Graphics · doi:10.1016/j.cag.2020.09.001 · Cited by 14

Estimation for monotone sampling

Cohen, E. (2014) · ACM Symposium on Principles of Distributed Computing (PODC) · doi:10.1145/2611462.2611485 · Cited by 13

Evaluating Different Methods for Automatically Collecting Large General Corpora for Basque from the Web

Leturia, I. (2012) · Cited by 13

Overview of the NTCIR-10 1CLICK-2 Task.

Kato, M. P., Ekstrand-Abueg, M., Pavlu, V., Sakai, T., Yamamoto, T., & Iwata, M. (2013) · NTCIR · Cited by 13

Predicting signatures of future malware variants

Howard, M., Pfeffer, A., Dalai, M., & Reposa, M. (2017) · International Conference on Malicious and Unwanted Software · doi:10.1109/malware.2017.8323965 · Cited by 13

Exponential time improvement for min-wise based algorithms

Feigenblat, G., Porat, E., & Shiftan, A. (2011) · doi:10.5555/2133036.2133041 · Cited by 13

Approximate Nearest-Neighbour Search with Inverted Signature Slice Lists

Chappell, T., Geva, S., & Zuccon, G. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16354-3_16 · Cited by 13

Structural Clustering of Machine-Generated Mail

Avigdor-Elgrabli, N., Cwalinski, M., Castro, D. D., Gamzu, I., Grabovitch-Zuyev, I., Lewin-Eytan, L., & Maarek, Y. (2016) · International Conference on Information and Knowledge Management · doi:10.1145/2983323.2983350 · Cited by 14

Fourth international workshop on uncovering plagiarism, authorship, and social software misuse

Stein, B., Potthast, M., Rosso, P., Barrón‐Cedeño, A., Stamatatos, E., & Koppel, M. (2011) · ACM SIGIR Forum · doi:10.1145/1988852.1988860 · Cited by 12

Collaborative data gathering in wireless sensor networks using measurement co-occurrence

Kalpakis, K., & Tang, S. (2008) · Computer Communications · doi:10.1016/j.comcom.2008.01.001 · Cited by 12

Optimal Hash Functions for Approximate Matches on the $n$-Cube

Gordon, D. M., Miller, V. S., & Ostapenko, P. (2010) · IEEE Transactions on Information Theory · doi:10.1109/tit.2009.2039037 · Cited by 12

Similarity based deduplication with small data chunks

Aronovich, L., Asher, R., Harnik, D., Hirsch, M., Klein, S. T., & Toaff, Y. (2015) · Discrete Applied Mathematics · doi:10.1016/j.dam.2015.09.018 · Cited by 12

Quantifying the ecological diversity and health of online news

Elejalde, E., Ferres, L., Herder, E., & Bollen, J. (2018) · Journal of Computational Science · doi:10.1016/j.jocs.2018.06.003 · Cited by 11

Automatic Detection of Local Reuse

Mittelbach, A., Lehmann, L., Rensing, C., & Steinmetz, R. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-16020-2_16 · Cited by 12

A novel method for detecting similar documents

Cooper, J. W., Coden, A. R., & Brown, E. W. (2003) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2002.994037 · Cited by 12

An ontology derived from heterogeneous sustainability indicator set documents

Ghahremanloo, L., Thom, J. A., & Magee, L. (2012) · doi:10.1145/2407085.2407095 · Cited by 11

Similarity Search for Dynamic Data Streams

Bury, M., Schwiegelshohn, C., & Sorella, M. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2916858 · Cited by 11

A cost‐efficient resemblance detection scheme for post‐deduplication delta compression in backup systems

Wang, C., Fu, Y., Yan, J., Wu, X., Zhang, Y., Xia, H., & Yuan, Y. (2021) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6558 · Cited by 11

An Intelligent Data De-duplication Based Backup System

Zhu, G., Zhang, X., Wang, L., Zhu, Y., & Dong, X. (2012) · International Conference on Network-Based Information Systems · doi:10.1109/nbis.2012.150 · Cited by 11

Robust and distributed web-scale near-dup document conflation in microsoft academic service

Wu, C., & Song, Y. (2015) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2015.7364059 · Cited by 10

Node Similarity with q -Grams for Real-World Labeled Networks

Conte, A., Ferraro, G., Grossi, R., Marino, A., Sadakane, K., & Uno, T. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220085 · Cited by 10

Efficient Sketching Algorithm for Sparse Binary Data

Pratap, R., Bera, D., & Revanuru, K. (2019) · Proceedings · doi:10.1109/icdm.2019.00061 · Cited by 10

Reducing information redundancy in search results

Plegas, Y., & Stamou, S. (2013) · ACM Symposium on Applied Computing · doi:10.1145/2480362.2480533 · Cited by 10

Palantir: Hierarchical Similarity Detection for Post-Deduplication Delta Compression

Huang, H., Wang, P., Su, Q., Xu, H. L., Xue, C. J., & Brinkmann, A. (2024) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3620665.3640353 · Cited by 9

Convolutional neural codes for image retrieval

Ou, X., Ling, H., Yan, L., & Liu, M. (2014) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2014.7041557 · Cited by 9

RMD: A Resemblance and Mergence Based Approach for High Performance Deduplication

Zhang, P., Huang, P. S., He, X., Wang, H., Yan, L., & Zhou, K. (2016) · Proceedings of the International Conference on Parallel Processing · doi:10.1109/icpp.2016.68 · Cited by 9

Streaming data

Szewczyk, W. (2010) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.130 · Cited by 9

Automated Extractions for Machine Generated Mail

Castro, D. D., Gamzu, I., Grabovitch-Zuyev, I., Lewin-Eytan, L., Pundir, A., Sahoo, N. R., & Viderman, M. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3186582 · Cited by 9

Clowns, Crowds, and Clouds: A Cross-Enterprise Approach to Detecting Information Leakage Without Leaking Information

Cooke, N. J., & Gillam, L. (2011) · Computer communications and networks · doi:10.1007/978-1-4471-2236-4_16 · Cited by 9

MedSearch

Luo, G., Tang, C., Yang, H., & Wei, X. (2007) · International World Wide Web Conference (WWW) · doi:10.1145/1242572.1242752 · Cited by 9

A Platform Framework for Cross-Lingual Text Relatedness Evaluation and Plagiarism Detection

Lee, C.-H., Wu, C.-H., & Yang, H.-C. (2008) · International Conference on Innovative Computing, Information and Control · doi:10.1109/icicic.2008.76 · Cited by 9

Bucket-size balancing locality sensitive hashing using the map reduce paradigm

Lee, K., Jeong, Y.-S., Lee, K. M., & Lee, K. M. (2017) · Cluster Computing · doi:10.1007/s10586-017-1013-2 · Cited by 9

Partial 3D object retrieval using local binary QUICCI descriptors and dissimilarity tree indexing

van Blokland, B. I., & Theoharis, T. (2021) · Computers & Graphics · doi:10.1016/j.cag.2021.07.018 · Cited by 9

Detecting Co-Derivative Documents in Large Text Collections

Pomikálek, J., & Rychlý, P. (2008) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2mjzft5m7wmp · Cited by 9

Detecting near-duplicate text documents with a hybrid approach

Varol, C., & Hari, S. (2015) · Journal of Information Science · doi:10.1177/0165551515577912 · Cited by 9

Web-Based Data Leakage Prevention.

Yoshihama, S., Mishina, T., & Matsumoto, T. (2010) · Cited by 9

Fast Comparative Analysis of Merge Trees Using Locality Sensitive Hashing

Lyu, W., Sridharamurthy, R., Phillips, J. M., & Wang, B. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456383 · Cited by 8

Exponential time improvement for min-wise based algorithms

Feigenblat, G., Porat, E., & Shiftan, A. (2011) · Information and Computation · doi:10.1016/j.ic.2011.01.005 · Cited by 8

A Mixed Generative-Discriminative Based Hashing Method

Zhang, Q., Wang, Y., Qian, J., & Huang, X. (2015) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2015.2507127 · Cited by 8

Streaming Algorithms for Robust Distinct Elements

Chen, D., & Zhang, Q. (2016) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/2882903.2882915 · Cited by 8

Evaluating text reuse discovery on the web

Chiu, S., Uysal, I., & Croft, W. B. (2010) · Information Interaction in Context Symposium (IIiX) · doi:10.1145/1840784.1840829 · Cited by 8

Rapid detection of similar peer-reviewed scientific papers via constant number of randomized fingerprints

HaCohen‐Kerner, Y., & Tayeb, A. (2016) · Information Processing & Management · doi:10.1016/j.ipm.2016.06.007 · Cited by 8

Effective and Fast Near Duplicate Detection via Signature-Based Compression Metrics

Zhang, X., Yao, Y., Ji, Y., & Fang, B. (2016) · Mathematical Problems in Engineering · doi:10.1155/2016/3919043 · Cited by 8

Efficient Compression Technique for Sparse Sets

Pratap, R., Sohony, I., & Kulkarni, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93040-4_14 · Cited by 8

A derandomization using min-wise independent permutations

Broder, A., Charikar, M., & Mitzenmacher, M. (2003) · Journal of Discrete Algorithms · doi:10.1016/s1570-8667(03)00003-0 · Cited by 8

Information redundancy across metadata collections

Foulonneau, M. (2006) · Information Processing & Management · doi:10.1016/j.ipm.2006.06.004 · Cited by 8

Trust-based Recommendations in Multi-layer Networks

Heß, C. (2008) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 8

ProbGraph: High-Performance and High-Accuracy Graph Mining with Probabilistic Set Representations

Besta, M., Miglioli, C., Labini, P. S., Tětek, J., Iff, P., Kanakagiri, R., Ashkboos, S., Janda, K., Podstawski, M., Kwaśniewski, G., et al. (2022) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41404.2022.00048 · Cited by 10

A Medical Multimodal Large Language Model for Pediatric Pneumonia

Tian, W., Huang, X., Cheng, T., He, W., Fang, J., Feng, R., Geng, D., & Zhang, X. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3569361 · Cited by 8

Get the most out of your sample

Cohen, E., & Kaplan, H. Y. (2011) · ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/1989284.1989288 · Cited by 7

The Design of a Lossless Deduplication Scheme to Eliminate Fine-Grained Redundancy for JPEG Image Storage Systems

Deng, C., Zou, X., Chen, Q., Tang, B., & Xia, W. (2024) · IEEE Transactions on Computers · doi:10.1109/tc.2024.3363456 · Cited by 7

A Near-Duplicate Detection Algorithm to Facilitate Document Clustering

Pamulaparty, L., Guru Rao, C. V., & Sreenivasa Rao, M. (2014) · International Journal of Data Mining & Knowledge Management Process · doi:10.5121/ijdkp.2014.4604 · Cited by 7

A Detailed Review on the Prominent Compression Methods Used for Reducing the Data Volume of Big Data

Anuradha, D., & Bhuvaneswari, S. (2016) · Annals of Data Science · doi:10.1007/s40745-016-0069-9 · Cited by 7

Clustering and load balancing optimization for redundant content removal

Zhu, S., Potapova, A., Alabduljalil, M., Liu, X., & Yang, T. (2012) · International World Wide Web Conference (WWW) · doi:10.1145/2187980.2187992 · Cited by 7

DCU@FIRE-2014

Ganguly, D., & Jones, G. J. F. (2015) · Annual Meeting of the Forum for Information Retrieval Evaluation (FIRE) · doi:10.1145/2824864.2824887 · Cited by 7

On enhancing the robustness of timeline summarization test collections

McCreadie, R., Rajput, S., Soboroff, I. M., Macdonald, C., & Ounis, I. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.02.006 · Cited by 7

Indexed variation graphs for efficient and accurate resistome profilingPreprint

Rowe, W. P. M., & Winn, M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/270835 · Cited by 7

Cross-Language Plagiarism Detection: A Case Study of European Languages Academic Works

Bakhteev, O., Chekhovich, Y. V., Grabovoy, A., Gorbachev, G., Gorlenko, T., Grashchenkov, K., Ivakhnenko, A. O., Kildyakov, A., Khazov, A., Komarnitsky, V., et al. (2022) · Ethics and integrity in educational contexts · doi:10.1007/978-3-031-16976-2_9 · Cited by 9

A nugget-based test collection construction paradigm

Rajput, S., Pavlu, V., Golbus, P. B., & Aslam, J. A. (2011) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2063576.2063861 · Cited by 6

Exponential Time Improvement for min-wise Based Algorithms

Feigenblat, G., Porat, E., & Shiftan, A. (2011) · ACM-SIAM Symposium on Discrete Algorithms (SODA) · doi:10.1137/1.9781611973082.5 · Cited by 6

XNDDF: Towards a Framework for Flexible Near-Duplicate Document Detection Using Supervised and Unsupervised Learning

Pamulaparty, L., Rao, C. V. G., & Rao, M. S. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.04.175 · Cited by 6

Efficient Approach for Near Duplicate Document Detection Using Textual and Conceptual Based Techniques

Roul, R. K., Mittal, S., & Joshi, P. (2014) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-07353-8_23 · Cited by 6

aiXcoder-7B: A Lightweight and Effective Large Language Model for Code Processing

Jiang, S., Li, J., Zong, H., Liu, H., Zhu, H., Hu, S., Li, E. M., Ding, J., Han, Y., Ning, W., et al. (2025) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip66354.2025.00025 · Cited by 16

Smart-to-Compress: A Predictive and Game-Theoretic Framework for Data Reduction Decisions

He, Z., Tian, W., Xie, Z., Zeng, D., Lu, J., Xu, Z., Xiao, W., & Wan, Y. (2026) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2026.3659933 · Cited by 5

Filtering near-duplicate documents

Broder, A. (1998)

Near duplicate detection in large document collections

Benčík, D. (2007) · Digital Repository (National Repository of Grey Literature)

Estimating the Number of Near-Duplicate Document Pairs for Massive Data Sets using Small Space

Deng, F., & Rafiei, D. (2008)

Concept based algorithm of dealing near-replicas of documents on the Web

Li, Z. (2006) · Journal of Computer Applications · Cited by 1

Near Duplicate Document Detection using Document Image

Uwamahoro, G., Zhang, Z., Mtafya, A. R., Li, W., & Jun, L. (2016) · International Journal of Multimedia and Ubiquitous Engineering · doi:10.14257/ijmue.2016.11.7.17