The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
The WEKA data mining software: an update
Open accessAbstract
More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These days, WEKA enjoys widespread acceptance in both academia and business, has an active community, and has been downloaded more than 1.4 million times since being placed on Source-Forge in April 2000. This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
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“The authors exploit several characteristics that can be gathered crawling an account's details, both from its profile and timeline. For each investigated account, such characteristics are exploited in a Random Forest algorithm [23, 43], that outputs if the account is a spambot or not.”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [43]
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“the Waikato Environment for Knowledge Analysis (Weka) tool is a relatively accessible and easy to use software package for applying machine learning algorithms for data mining tasks [32].”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [32]
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Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Self-citationClassification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,123
Witten, I. H. (2008) · Cited by 9,143
Self-citationLIBLINEAR: A Library for Large Linear Classification
Fan, R.-E., Chang, K., Hsieh, C., Xiang-rui, W., & Lin, C. (2008) · doi:10.5555/1390681.1442794 · Cited by 6,684
Witten, I. H., & Frank, E. (2002) · ACM SIGMOD Record · doi:10.1145/507338.507355 · Cited by 5,176
Self-citationSolving the multiple instance problem with axis-parallel rectangles
Dietterich, T. G., Lathrop, R. H., & Lozano-Pérez, T. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(96)00034-3 · Cited by 2,866
Rotation Forest: A New Classifier Ensemble Method
Rodríguez, J. J., Kuncheva, L. I., & Alonso, C. J. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.211 · Cited by 1,931
Random sampling with a reservoir
Vitter, J. S. (1985) · ACM Transactions on Mathematical Software · doi:10.1145/3147.3165 · Cited by 1,789
GAUSSIAN PROCESSES FOR MACHINE LEARNING
Seeger, M. (2004) · International Journal of Neural Systems · doi:10.1142/s0129065704001899 · Cited by 1,597
Department of Computer Science
Buisman, H., Gomez-rodriguez, M., & Hassanpour, S. (1993) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(93)90083-n · Cited by 1,212
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., & Euler, T. (2006) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1150402.1150531 · Cited by 1,010
Witten, I. H., Paynter, G. W., Frank, E., Gutwin, C. A., & Nevill-Manning, C. G. (1999) · doi:10.1145/313238.313437 · Cited by 860
Self-citationLarge-Scale Bayesian Logistic Regression for Text Categorization
Genkin, A., Lewis, D. D., & Madigan, D. (2007) · Technometrics · doi:10.1198/004017007000000245 · Cited by 812
Kepler: An Extensible System for Design and Execution of Scientific Workflows
Altintas, I., Berkley, C., Jaeger, E., Jones, M., Ludäscher, B., & Mock, S. (2004) · Cited by 745
da Gama, J. M. P. (2004) · Machine Learning · doi:10.1023/b:mach.0000027782.67192.13 · Cited by 287
Unsupervised document classification using sequential information maximization
Slonim, N., Friedman, N., & Tishby, N. (2002) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/564376.564401 · Cited by 250
Best-first Decision Tree Learning
Shi, H. (2007) · Research Commons (University of Waikato) · Cited by 245
Stacking Bagged and Dagged Models
Ting, K. M., & Witten, I. H. (1997) · Cited by 202
Self-citationYang, Y., Guan, X., & You, J. (2002) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/775047.775149 · Cited by 166
Comparative Evaluation of Approaches to Propositionalization
Krogel, M.-A., Rawles, S., Železný, F., Flach, P. A., Nada Lavrač, A., & Wrobel, S. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39917-9_14 · Cited by 125
Combining Naive Bayes and Decision Tables
Hall, M. A., & Frank, E. (2008) · Research Commons (University of Waikato) · Cited by 125
Self-citationEnsembles of nested dichotomies for multi-class problems
Frank, E., & Krämer, S. (2004) · doi:10.1145/1015330.1015363 · Cited by 121
Self-citationDiscriminative parameter learning for Bayesian networks
Su, J., Zhang, H., Ling, C. X., & Matwin, S. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390284 · Cited by 119
Weka4WS: A WSRF-Enabled Weka Toolkit for Distributed Data Mining on Grids
Talia, D., Trunfio, P., & Verta, O. (2005) · Lecture notes in computer science · doi:10.1007/11564126_32 · Cited by 87
An Optimization Perspective on Kernel Partial Least Squares Regression
Bennett, K. P. (2003) · Cited by 77
Weightily Averaged One-Dependence Estimators
Jiang, L., & Zhang, H. (2006) · Lecture notes in computer science · doi:10.1007/978-3-540-36668-3_116 · Cited by 73
Ensembles of Balanced Nested Dichotomies for Multi-class Problems
Dong, L., Frank, E., & Krämer, S. (2005) · Lecture notes in computer science · doi:10.1007/11564126_13 · Cited by 68
Self-citationGATE: an environment to support research and development in natural language engineering
Gaizauskas, R., Cunningham, H., Wilks, Y. A., et al. (2005) · Proceedings - International Conference on Tools with Artificial Intelligence, TAI · doi:10.1109/tai.1996.560401 · Cited by 65
Efficient lazy elimination for averaged one-dependence estimators
Zheng, F., & Webb, G. I. (2006) · International Conference on Machine Learning · doi:10.1145/1143844.1143984 · Cited by 60
BioWeka—extending the Weka framework for bioinformatics
Gewehr, J. E., Szugat, M., & Zimmer, R. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btl671 · Cited by 57
Mayday-a microarray data analysis workbench
Dietzsch, J., Gehlenborg, N., & Nieselt, K. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl070 · Cited by 55
Statistical Learning in Multiple Instance Problems
Xu, X. (2003) · Research Commons (University of Waikato) · Cited by 54
Research works printed but not linked: 15
- 10.5555/998688.1007097 · DOI
- S. Celis and D.R. Musicant . Weka-parallel: machine learning in parallel. Technical report , Carleton College , CS TR , 2002 . S. Celis and D.R. Musicant. Weka-parallel: machine learning in parallel. Technical report, Carleton College, CS TR, 2002.
- 10.5555/3121445.3121460 · DOI
- 10.5555/850949.853624 · DOI
- A. Genkin , D.D. Lewis , and D. Madigan . Largescale bayesian logistic regression for text categorization. Technical report , DIMACS , 2004 . A. Genkin, D.D. Lewis, and D. Madigan. Largescale bayesian logistic regression for text categorization. Technical report, DIMACS, 2004.
- K. Hornik , A. Zeileis , T. Hothorn , and C. Buchta . RWeka: An R Interface to Weka , 2009 . R package version 0.3-16. K. Hornik, A. Zeileis, T. Hothorn, and C. Buchta. RWeka: An R Interface to Weka, 2009. R package version 0.3-16.
- L. Jiang and H. Zhang . Weightily averaged onedependence estimators . In Proceedings of the 9th Biennial Pacific Rim International Conference on Artificial Intelligence, PRICAI 2006 , volume 4099 of LNAI, pages 970 -- 974 , 2006 . L. Jiang and H. Zhang. Weightily averaged onedependence estimators. In Proceedings of the 9th Biennial Pacific Rim International Conference on Artificial Intelligence, PRICAI 2006, volume 4099 of LNAI, pages 970--974, 2006.
- R. Khoussainov , X. Zuo , and N. Kushmerick . Gridenabled Weka: A toolkit for machine learning on the grid . ERCIM News , 59 , 2004 . R. Khoussainov, X. Zuo, and N. Kushmerick. Gridenabled Weka: A toolkit for machine learning on the grid. ERCIM News, 59, 2004.
- M.-A. Krogel and S. Wrobel . Facets of aggregation approaches to propositionalization . In T. Horvath and A. Yamamoto, editors, Work-in-Progress Track at the Thirteenth International Conference on Inductive Logic Programming (ILP) , 2003 . M.-A. Krogel and S. Wrobel. Facets of aggregation approaches to propositionalization. In T. Horvath and A. Yamamoto, editors, Work-in-Progress Track at the Thirteenth International Conference on Inductive Logic Programming (ILP), 2003.
- D. Nadeau . Balie-baseline information extraction : Multilingual information extraction from text with machine learning and natural language techniques. Technical report , University of Ottawa , 2005 . D. Nadeau. Balie-baseline information extraction : Multilingual information extraction from text with machine learning and natural language techniques. Technical report, University of Ottawa, 2005.
- G. Piatetsky-Shapiro. KDnuggets news on SIGKDD service award. http://www.kdnuggets.com/news/2005/n13/2i.html 2005. G. Piatetsky-Shapiro. KDnuggets news on SIGKDD service award. http://www.kdnuggets.com/news/2005/n13/2i.html 2005.
- C. Shearer . The CRISP-DM model: The new blueprint for data mining . Journal of Data Warehousing , 5 ( 4 ), 2000 . C. Shearer. The CRISP-DM model: The new blueprint for data mining. Journal of Data Warehousing, 5(4), 2000.
- 10.5555/3121445.3121479 · DOI
- I.H. Witten and E. Frank . Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations . Morgan Kaufmann , San Francisco , 2000 . I.H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco, 2000.
- I.H. Witten , G.W. Paynter , E. Frank , C. Gutwin , and C.G. Nevill-Manning . Kea: Practical automatic keyphrase extraction . In Y.-L . Theng and S. Foo, editors, Design and Usability of Digital Libraries: Case Studies in the Asia Pacific, pages 129 -- 152 . Information Science Publishing , London, 2005 . I.H. Witten, G.W. Paynter, E. Frank, C. Gutwin, and C.G. Nevill-Manning. Kea: Practical automatic keyphrase extraction. In Y.-L. Theng and S. Foo, editors, Design and Usability of Digital Libraries: Case Studies in the Asia Pacific, pages 129--152. Information Science Publishing, London, 2005.
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Metadata
- Authors
- Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H.
- Affiliations
- Pentaho Corporation, Orlando, FL
- University of Waikato (NZ)
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2009-11-16
- Volume
- 11
- Issue
- 1
- Pages
- 10-18
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W2133990480
- Cited by
- 17,750
- References
- 37
- Citations by year
- 2026: 93 · 2025: 198 · 2024: 252 · 2023: 342 · 2022: 473 · 2021: 742 · 2020: 949 · 2019: 1162 · 2018: 1382 · 2017: 1550 · 2016: 2009 · 2015: 2093 · 2014: 2031 · 2013: 1714 · 2012: 1353
- FWCI
- 2427.11
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- ACM SIGKDD Explorations Newsletter (publishedVersion)
- Research Commons (University of Waikato) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- WEKA, data mining tools, software release history, open source software
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