Weka (machine learning)

"WEKA" redirects here. For other uses, see Weka (disambiguation).
Weka

Weka logo, featuring weka, a bird endemic to New Zealand

Weka 3.5.5 with Explorer window open with Iris UCI dataset
Developer(s) University of Waikato
Stable release 3.8.0 (stable) / April 14, 2016 (2016-04-14)
Preview release 3.9.0 / April 14, 2016 (2016-04-14)
Written in Java
Operating system Windows, OS X, Linux
Platform IA-32, x86-64; Java SE
Type Machine learning
License GNU General Public License
Website www.cs.waikato.ac.nz/~ml/weka

Waikato Environment for Knowledge Analysis (Weka) is a popular suite of machine learning software written in Java, developed at the University of Waikato, New Zealand. It is free software licensed under the GNU General Public License.

Description

Weka (pronounced to rhyme with Mecca) is a workbench[1] that contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to these functions. 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:

Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection. All of Weka's techniques are predicated on the assumption that the data is available as one flat file or relation, where each data point is described by a fixed number of attributes (normally, numeric or nominal attributes, but some other attribute types are also supported). Weka provides access to SQL databases using Java Database Connectivity and can process the result returned by a database query. It is not capable of multi-relational data mining, but there is separate software for converting a collection of linked database tables into a single table that is suitable for processing using Weka.[4] Another important area that is currently not covered by the algorithms included in the Weka distribution is sequence modeling.

User interfaces

Weka's main user interface is the Explorer, but essentially the same functionality can be accessed through the component-based Knowledge Flow interface and from the command line. There is also the Experimenter, which allows the systematic comparison of the predictive performance of Weka's machine learning algorithms on a collection of datasets.

The Explorer interface features several panels providing access to the main components of the workbench:

Extension packages

In version 3.7.2 (thus not available in the stable "book" version of Weka), a package manager was added to allow the easier installation of extension packages.[5] Some functionality that used to be included with Weka prior to this version has since been moved into such extension packages, but this change also makes it easier for other to contribute extensions to Weka and to maintain the software, as this modular architecture allows independent updates of the Weka core and individual extensions.

History

Related tools

See also

References

  1. Ian H. Witten; Eibe Frank; Mark A. Hall (2011). "Data Mining: Practical machine learning tools and techniques, 3rd Edition". Morgan Kaufmann, San Francisco. Retrieved 2011-01-19.
  2. G. Holmes; A. Donkin; I.H. Witten (1994). "Weka: A machine learning workbench" (PDF). Proc Second Australia and New Zealand Conference on Intelligent Information Systems, Brisbane, Australia. Retrieved 2007-06-25.
  3. S.R. Garner; S.J. Cunningham; G. Holmes; C.G. Nevill-Manning; I.H. Witten (1995). "Applying a machine learning workbench: Experience with agricultural databases" (PDF). Proc Machine Learning in Practice Workshop, Machine Learning Conference, Tahoe City, CA, USA. pp. 14–21. Retrieved 2007-06-25.
  4. P. Reutemann; B. Pfahringer; E. Frank (2004). "Proper: A Toolbox for Learning from Relational Data with Propositional and Multi-Instance Learners". 17th Australian Joint Conference on Artificial Intelligence (AI2004). Springer-Verlag. Retrieved 2007-06-25.
  5. "weka - How do I use the package manager?". Retrieved 20 September 2014.
  6. Ian H. Witten; Eibe Frank; Len Trigg; Mark Hall; Geoffrey Holmes; Sally Jo Cunningham (1999). "Weka: Practical Machine Learning Tools and Techniques with Java Implementations" (PDF). Proceedings of the ICONIP/ANZIIS/ANNES'99 Workshop on Emerging Knowledge Engineering and Connectionist-Based Information Systems. pp. 192–196. Retrieved 2007-06-26.
  7. Gregory Piatetsky-Shapiro (2005-06-28). "KDnuggets news on SIGKDD Service Award 2005". Retrieved 2007-06-25.
  8. "Overview of SIGKDD Service Award winners". 2005. Retrieved 2007-06-25.
  9. http://sourceforge.net/top/topalltime.php?type=downloads&offset=200

External links

Wikimedia Commons has media related to Weka (machine learning).


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