Topic · Data Stream Mining Techniques
Most cited papers in Data Stream Mining Techniques
What are the most cited papers in Data Stream Mining Techniques?
The most cited paper in Data Stream Mining Techniques is "Anomaly detection" (Varun Chandola, Arindam Banerjee, Vipin Kumar, 2009), with 11,623 citations. Next come "Machine learning: Trends, perspectives, and prospects" (10,020) and "A survey of transfer learning" (6,327).
Showing 50 of 99 papers. Every paper with more than 846 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Anomaly detection Varun Chandola, Arindam Banerjee, Vipin Kumar · ACM Computing Surveys | Jul 2009 | 11,623 |
| 2 | Machine learning: Trends, perspectives, and prospects Michael I. Jordan, Tom M. Mitchell · Science | 16 Jul 2015 | 10,020 |
| 3 | A survey of transfer learning Karl R. Weiss, Taghi M. Khoshgoftaar, Dingding Wang · Journal Of Big Data | 27 May 2016 | 6,327 |
| 4 | Machine Learning: Algorithms, Real-World Applications and Research Directions Iqbal H. Sarker · SN Computer Science | 22 Mar 2021 | 5,413 |
| 5 | Data mining and knowledge discovery: making sense out of data U.M. Feyyad · IEEE Expert | Oct 1996 | 4,643 |
| 6 | A review on genetic algorithm: past, present, and future Sourabh Katoch, Sumit Singh Chauhan, Vijay Kumar · Multimedia Tools and Applications | 31 Oct 2020 | 4,634 |
| 7 | BPR: Bayesian Personalized Ranking from Implicit Feedback Steffen Rendle, Christoph Freudenthaler, Zeno Gantner et al. · arXiv (Cornell University) | 9 May 2012 | 4,399 |
| 8 | Spark: cluster computing with working sets Matei Zaharia, Mosharaf Chowdhury, Michael J. M. Franklin et al. | 22 Jun 2010 | 4,222 |
| 9 | Instance-Based Learning Algorithms David W. Aha, Dennis F. Kibler, Marc K. Albert · Machine Learning | 1991 | 4,133 |
| 10 | BIRCH Tian Yao Zhang, Raghu Ramakrishnan, Miron Livny | 1996 | 3,923 |
| 11 | Technical Note: Q-Learning Christopher J. C. H. Watkins, Peter Dayan · Machine Learning | May 1992 | 3,822 |
| 12 | A survey on concept drift adaptation João Manuel Portela da Gama, Indrė Žliobaitė, Albert Bifet et al. · ACM Computing Surveys | Mar 2014 | 3,634 |
| 13 | Tracking-Learning-Detection Zdenek Kalal, Krystian Mikolajczyk, Jiřı́ Matas · IEEE Transactions on Pattern Analysis and Machine Intelligence | 13 Dec 2011 | 3,308 |
| 14 | Ensemble learning: A survey Omer Sagi, Lior Rokach · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery | 27 Feb 2018 | 3,107 |
| 15 | Tracking and Data Association Yaakov Bar‐Shalom, Thomas E. Fortmann, Peter G. Cable · The Journal of the Acoustical Society of America | Feb 1990 | 3,088 |
| 16 | Ensemble based systems in decision making Robi Polikar · IEEE Circuits and Systems Magazine | 2006 | 2,972 |
| 17 | Big Data: A Survey Min Chen, Shiwen Mao, Yunhao Liu · Mobile Networks and Applications | 20 Jan 2014 | 2,903 |
| 18 | Deep learning applications and challenges in big data analytics Maryam Mousaarab Najafabadi, Flavio Villanustre, Taghi M. Khoshgoftaar et al. · Journal Of Big Data | 23 Feb 2015 | 2,613 |
| 19 | Cost-effective outbreak detection in networks Jure Leskovec, Andreas Krause, Carlos Guestrin et al. | 12 Aug 2007 | 2,547 |
| 20 | An Algorithmic Framework for Performing Collaborative Filtering Jonathan L. Herlocker, Joseph A. Konstan, Al Borchers et al. · ACM SIGIR Forum | 2 Aug 2017 | 2,545 |
| 21 | Models and issues in data stream systems Brian Babcock, Shivnath Babu, Mayur Datar et al. | 3 Jun 2002 | 2,517 |
| 22 | Learning to forget: continual prediction with LSTM Felix A. Gers | 1999 | 2,448 |
| 23 | Data mining with big data Xindong Wu, Xingquan Zhu, Gongqing Wu et al. · IEEE Transactions on Knowledge and Data Engineering | 26 Jun 2013 | 2,429 |
| 24 | When Is “Nearest Neighbor” Meaningful? Kevin S. Beyer, Jonathan Goldstein, Raghu Ramakrishnan et al. · Lecture notes in computer science | 1999 | 2,347 |
| 25 | Mining high-speed data streams Pedro Domingos, Geoff Hulten | Aug 2000 | 2,210 |
| 26 | Anchors: High-Precision Model-Agnostic Explanations Marco Túlio Ribeiro, Sameer Kumar Singh, Carlos Guestrin · Proceedings of the AAAI Conference on Artificial Intelligence | 25 Apr 2018 | 2,143 |
| 27 | Deep Interest Network for Click-Through Rate Prediction Guorui Zhou, Xiaoqiang Zhu, Chenru Song et al. | 19 Jul 2018 | 2,092 |
| 28 | Predicting the Future with Social Media Sitaram Asur, Bernardo A. Huberman | Aug 2010 | 2,074 |
| 29 | A survey on ensemble learning Dong Xibin, Zhiwen Yu, Wenming Cao et al. · Frontiers of Computer Science | 30 Aug 2019 | 1,956 |
| 30 | Classification Based on Decision Tree Algorithm for Machine Learning Bahzad Charbuty, Adnan Mohsin Abdulazeez · Journal of Applied Science and Technology Trends | 24 Mar 2021 | 1,879 |
| 31 | Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review Ahmed G. Gad · Archives of Computational Methods in Engineering | 19 Apr 2022 | 1,869 |
| 32 | Learning from Time-Changing Data with Adaptive Windowing Albert Bifet, Ricard Gavaldà | 26 Apr 2007 | 1,783 |
| 33 | Mining time-changing data streams Geoff Hulten, Laurie A. Spencer, Pedro Domingos | 26 Aug 2001 | 1,764 |
| 34 | Explaining collaborative filtering recommendations Jonathan L. Herlocker, Joseph A. Konstan, John Riedl | Dec 2000 | 1,715 |
| 35 | The k-means Algorithm: A Comprehensive Survey and Performance Evaluation Mohiuddin Ahmed, Raihan Seraj, Syed Mohammed Shamsul Islam · Electronics | 12 Aug 2020 | 1,709 |
| 36 | A review of novelty detection Marco A. F. Pimentel, David Andrew Clifton, Lei Clifton et al. · Signal Processing | 3 Jan 2014 | 1,671 |
| 37 | Support Vector Machine Shan Suthaharan · Integrated series on information systems/Integrated series in information systems | 20 Oct 2015 | 1,607 |
| 38 | Decision trees: a recent overview Sotiris B. Kotsiantis · Artificial Intelligence Review | 28 Jun 2011 | 1,590 |
| 39 | Transfer Learning for Reinforcement Learning Domains: A Survey Matthew Edmund Taylor, Peter Stone · Journal of Machine Learning Research | Dec 2009 | 1,562 |
| 40 | Learning with Drift Detection João Manuel Portela da Gama, Pedro Medas, Gladys Castillo et al. · Lecture notes in computer science | 2004 | 1,545 |
| 41 | Algorithms for Mining Distance-Based Outliers in Large Datasets Edwin M. Knorr, Raymond T. Ng | 24 Aug 1998 | 1,528 |
| 42 | Learning in the Presence of Concept Drift and Hidden Contexts Gerhard Widmer, Miroslav Kubát · Machine Learning | Apr 1996 | 1,454 |
| 43 | Online Passive-Aggressive Algorithms Koby Crammer, Ofer Dekel, Joseph Keshet et al. | 2003 | 1,443 |
| 44 | Latent semantic models for collaborative filtering Thomas Frank Hofmann · ACM Transactions on Information Systems | 2004 | 1,389 |
| 45 | Finding frequent items in data streams Moses Charikar, Kevin Chen, Martı́n Farach-Colton · Theoretical Computer Science | 12 Aug 2003 | 1,333 |
| 46 | Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications Thanh Thi Nguyen, Ngoc Duy Nguyen, Saeid Nahavandi · IEEE Transactions on Cybernetics | 20 Mar 2020 | 1,301 |
| 47 | A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects Ibomoiye Domor Mienye, Yanxia Sun · IEEE Access | 2022 | 1,297 |
| 48 | Mining concept-drifting data streams using ensemble classifiers Haixun Wang, Wei Fan, Philip S. Yu et al. | 24 Aug 2003 | 1,275 |
| 49 | Big Data Deep Learning: Challenges and Perspectives Xuewen Chen, Xiaotong Lin · IEEE Access | 2014 | 1,267 |
| 50 | The design of the borealis stream processing engine Daniel J. Abadi, Yanif Ahmad, Magdalena Bałazińska et al. | 2005 | 1,229 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.