Waikato Environment for Knowledge Analysis (Weka) System
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A Waikato Environment for Knowledge Analysis (Weka) System is a Java-based data mining system produced by the Weka Project.
- AKA: Weka System.
- Context:
- Website: https://svn.cms.waikato.ac.nz/svn/weka/
- It was developed by University of Waikato.
- Example(s):
- Counter-Example(s):
- See: Machine Learning System, Data Mining Task.
References
2019
- (Frank et al., 2019) ⇒ (April, 2019). Weka 3: Data Mining Software in Java. In: Eibe Frank, Mark A. Hall, and Ian H. Witten (2016). The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, 2016.
- Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization.
Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this.
Weka is open source software issued under the GNU General Public License.
We have put together several free online courses that teach machine learning and data mining using Weka. Check out the website for the courses for details on when and how to enrol. The videos for the courses are available on Youtube.
Yes, it is possible to apply Weka to process big data [1] and perform deep learning [2]!
- Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization.
2016
- (Witten et al., 2016) ⇒ Ian H. Witten, Eibe Frank, and Christopher J. Pal. (2016). “Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition". Morgan Kaufmann,2016, 2016. ISBN: 0128043571, 9780128043578.
2013a
- (Thornton et al., 2013) ⇒ Chris Thornton, Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown (2013). "Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms". In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD '13).
2013b
- (Wikipedia, 2013a) ⇒ http://en.wikipedia.org/wiki/Weka_(machine_learning)
- Weka (Waikato Environment for Knowledge Analysis) is a popular suite of machine learning software written in Java, developed at the University of Waikato, New Zealand. Weka is free software available under the GNU General Public License.
2013c
- (Wikipedia, 2013a) ⇒ http://en.wikipedia.org/wiki/Weka_%28machine_learning%29#Description
- The Weka (pronounced Way-Kuh) workbench[1] contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to this functionality. The original non-Java version of Weka was a TCL/TK front-end to (mostly third-party) modeling algorithms implemented in other programming languages, plus data preprocessing utilities in C, and a Makefile-based system for running machine learning experiments. This original version was primarily designed as a tool for analyzing data from agricultural domains,[2][3] but the more recent fully Java-based version (Weka 3), for which development started in 1997, is now used in many different application areas, in particular for educational purposes and research. Advantages of Weka include:
- free availability under the GNU General Public License.
- portability, since it is fully implemented in the Java programming language and thus runs on almost any modern computing platform
- a comprehensive collection of data preprocessing and modeling techniques
- ease of use due to its graphical user interfaces
- The Weka (pronounced Way-Kuh) workbench[1] contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to this functionality. The original non-Java version of Weka was a TCL/TK front-end to (mostly third-party) modeling algorithms implemented in other programming languages, plus data preprocessing utilities in C, and a Makefile-based system for running machine learning experiments. This original version was primarily designed as a tool for analyzing data from agricultural domains,[2][3] but the more recent fully Java-based version (Weka 3), for which development started in 1997, is now used in many different application areas, in particular for educational purposes and research. Advantages of Weka include:
- ↑ Ian H. Witten; Eibe Frank, Mark A. Hall (2011). "Data Mining: Practical machine learning tools and techniques, 3rd Edition". Morgan Kaufmann, San Francisco. http://www.cs.waikato.ac.nz/~ml/weka/book.html. Retrieved 2011-01-19.
- ↑ G. Holmes; A. Donkin and I.H. Witten (1994). "Weka: A machine learning workbench". Proc Second Australia and New Zealand Conference on Intelligent Information Systems, Brisbane, Australia. http://www.cs.waikato.ac.nz/~ml/publications/1994/Holmes-ANZIIS-WEKA.pdf. Retrieved 2007-06-25.
- ↑ S.R. Garner; S.J. Cunningham, G. Holmes, C.G. Nevill-Manning, and I.H. Witten (1995). "Applying a machine learning workbench: Experience with agricultural databases". Proc Machine Learning in Practice Workshop, Machine Learning Conference, Tahoe City, CA, USA. pp. 14–21. http://www.cs.waikato.ac.nz/~ml/publications/1995/Garner95-imlc95.pdf. Retrieved 2007-06-25.
2011
- (Witten et al., 2016) ⇒ Ian H. Witten, Eibe Frank, and Mark A. Hall. (2011). “Data Mining: Practical Machine Learning Tools and Techniques, Third Edition". Elsevier, 2011. ISBN: 0080890369, 978008089036
2005
- (Witten & Frank, 2005) ⇒ Ian H. Witten, and Eibe Frank. (2005). “Data Mining: Practical machine learning tools and techniques, Second Edition." Elsevier, 2005. ISBN: 008047702X, 9780080477022].
2000
- (Witten & Frank, 2000) ⇒ Ian H. Witten, and Eibe Frank. (2000). “Data Mining: Practical Machine Learning Tools and Techniques with Java implementations." Morgan Kaufmann.