Witten, Ian H.Frank, EibeTrigg, Leonard E.Hall, Mark A.Holmes, GeoffreyCunningham, Sally Jo2008-10-172008-10-171999-08Witten, I.H., Frank, E., Trigg, L., Hall, M., Holmes, G. & Cunningham, S.J. (1999). Weka: Practical machine learning tools and techniques with Java implementations. (Working paper 99/11). Hamilton, New Zealand: University of Waikato, Department of Computer Science.https://hdl.handle.net/10289/1040The Waikato Environment for Knowledge Analysis (Weka) is a comprehensive suite of Java class libraries that implement many state-of-the-art machine learning and data mining algorithms. Weka is freely available on the World-Wide Web and accompanies a new text on data mining [1] which documents and fully explains all the algorithms it contains. Applications written using the Weka class libraries can be run on any computer with a Web browsing capability; this allows users to apply machine learning techniques to their own data regardless of computer platform.application/pdfencomputer scienceMachine learningWorking Paper