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.







  • (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:
  1. 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. 
  2. 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. 
  3. 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.