Abstract
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.
- I. Altintas, C. Berkley, E. Jaeger, M. Jones, B. Ludscher, and S. Mock. Kepler: An extensible system for design and execution of scientific workflows. In In SSDBM, pages 21--23, 2004. Google Scholar
Digital Library
- K. Bennett and M. Embrechts. An optimization perspective on kernel partial least squares regression. In J.S. et al., editor, Advances in Learning Theory: Methods, Models and Applications, volume 190 of NATO Science Series, Series III: Computer and System Sciences, pages 227--249. IOS Press, Amsterdam, The Netherlands, 2003.Google Scholar
- L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone. Classification and Regression Trees. Wadsworth International Group, Belmont, California, 1984.Google Scholar
- S. Celis and D.R. Musicant. Weka-parallel: machine learning in parallel. Technical report, Carleton College, CS TR, 2002.Google Scholar
- C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.Google Scholar
- T.G. Dietterich, R.H. Lathrop, and T. Lozano-Pérez. Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell., 89(1-2):31--71, 1997. Google Scholar
Digital Library
- J. Dietzsch, N. Gehlenborg, and K. Nieselt. Maydaya microarray data analysis workbench. Bioinformatics, 22(8):1010--1012, 2006. Google Scholar
Digital Library
- L. Dong, E. Frank, and S. Kramer. Ensembles of balanced nested dichotomies for multi-class problems. In Proc 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, pages 84--95. Springer, 2005. Google Scholar
Digital Library
- R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. Journal of Machine Learning. Research, 9:1871--1874, 2008. Google Scholar
Digital Library
- E. Frank and S. Kramer. Ensembles of nested dichotomies for multi-class problems. In Proc 21st International Conference on Machine Learning, Banff, Canada, pages 305--312. ACM Press, 2004. Google Scholar
Digital Library
- R. Gaizauskas, H. Cunningham, Y. Wilks, P. Rodgers, and K. Humphreys. GATE: an environment to support research and development in natural language engineering. In In Proceedings of the 8th IEEE International Conference on Tools with Artificial Intelligence, pages 58--66, 1996. Google Scholar
Digital Library
- J. Gama. Functional trees. Machine Learning, 55(3):219--250, 2004. Google Scholar
Digital Library
- A. Genkin, D.D. Lewis, and D. Madigan. Largescale bayesian logistic regression for text categorization. Technical report, DIMACS, 2004.Google Scholar
- J.E. Gewehr, M. Szugat, and R. Zimmer. BioWeka-extending the weka framework for bioinformatics. Bioinformatics, 23(5):651--653, 2007. Google Scholar
Digital Library
- M. Hall and E. Frank. Combining naive Bayes and decision tables. In Proc 21st Florida Artificial Intelligence Research Society Conference, Miami, Florida. AAAI Press, 2008.Google Scholar
- K. Hornik, A. Zeileis, T. Hothorn, and C. Buchta. RWeka: An R Interface to Weka, 2009. R package version 0.3-16.Google Scholar
- 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. Google Scholar
Digital Library
- R. Khoussainov, X. Zuo, and N. Kushmerick. Gridenabled Weka: A toolkit for machine learning on the grid. ERCIM News, 59, 2004.Google Scholar
- 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.Google Scholar
- I. Mierswa, M. Wurst, R. Klinkenberg, M. Scholz, and T. Euler. Yale: Rapid prototyping for complex data mining tasks. In L. Ungar, M. Craven, D. Gunopulos, and T. Eliassi-Rad, editors, KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 935--940, New York, NY, USA, August 2006. ACM. Google Scholar
Digital Library
- D. Nadeau. Balie-baseline information extraction : Multilingual information extraction from text with machine learning and natural language techniques. Technical report, University of Ottawa, 2005.Google Scholar
- G. Piatetsky-Shapiro. KDnuggets news on SIGKDD service award. http://www.kdnuggets.com/news/2005/n13/2i.html, 2005.Google Scholar
- R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2006. ISBN 3-900051-07-0.Google Scholar
- J.J. Rodriguez, L.I. Kuncheva, and C.J. Alonso. Rotation forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(10):1619--1630, 2006. Google Scholar
Digital Library
- K. Sandberg. The haar wavelet transform. http://amath.colorado.edu/courses/5720/2000Spr/Labs/Haar/haar.html, 2000.Google Scholar
- M. Seeger. Gaussian processes for machine learning. International Journal of Neural Systems, 14:2004, 2004.Google Scholar
Cross Ref
- C. Shearer. The CRISP-DM model: The new blueprint for data mining. Journal of Data Warehousing, 5(4), 2000.Google Scholar
- H. Shi. Best-first decision tree learning. Master's thesis, University of Waikato, Hamilton, NZ, 2007. COMP594.Google Scholar
- N. Slonim, N. Friedman, and N. Tishby. Unsupervised document classification using sequential information maximization. In Proceedings of the 25th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 129--136, 2002. Google Scholar
Digital Library
- J. Su, H. Zhang, C.X. Ling, and S. Matwin. Discriminative parameter learning for bayesian networks. In ICML 2008, 2008. Google Scholar
Digital Library
- D. Talia, P. Trunfio, and O. Verta. Weka4ws: a wsrfenabled weka toolkit for distributed data mining on grids. In Proc. of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2005, pages 309--320. Springer-Verlag, 2005. Google Scholar
Digital Library
- K.M. Ting and I.H. Witten. Stacking bagged and dagged models. In D. H. Fisher, editor, Fourteenth international Conference on Machine Learning, pages 367--375, San Francisco, CA, 1997. Morgan Kaufmann Publishers. Google Scholar
Digital Library
- J.S. Vitter. Random sampling with a reservoir. ACM Transactions on Mathematical Software, 11(1):37--57, 1985. Google Scholar
Digital Library
- I.H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco, 2000. Google Scholar
Digital Library
- I.H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco, 2 edition, 2005. Google Scholar
Digital Library
- 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.Google Scholar
- X. Xu. Statistical learning in multiple instance problems. Master's thesis, Department of Computer Science, University of Waikato, 2003.Google Scholar
- Y. Yang, X. Guan, and J. You. CLOPE: a fast and effective clustering algorithm for transactional data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 682--687. ACM New York, NY, USA, 2002. Google Scholar
Digital Library
- F. Zheng and G.I. Webb. Efficient lazy elimination for averaged-one dependence estimators. In Proceedings of the Twenty-third International Conference on Machine Learning (ICML 2006), pages 1113--1120. ACM Press, 2006. Google Scholar
Digital Library
Index Terms
The WEKA data mining software: an update
Recommendations
Mining of frequent itemsets with JoinFI-mine algorithm
AIKED'11: Proceedings of the 10th WSEAS international conference on Artificial intelligence, knowledge engineering and data basesAssociation rule mining among frequent items has been widely studied in data mining field. Many researches have improved the algorithm for generation of all the frequent itemsets. In this paper, we proposed a new algorithm to mine all frequents itemsets ...
TreeITL-Mine: Mining Frequent Itemsets Using Pattern Growth, Tid Intersection, and Prefix Tree
AI '02: Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial IntelligenceAn important problem in data mining is the discovery of association rules that identify relationships among sets of items. Finding frequent itemsets is computationally the most expensive step in association rules mining, and so most of the research ...
Efficient mining of indirect associations using HI-mine
AI'03: Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligenceDiscovering association rules is one of the important tasks in data mining. While most of the existing algorithms are developed for efficient mining of frequent patterns, it has been noted recently that some of the infrequent patterns, such as indirect ...






Comments