Weka-A Machine Learning Workbench for Data Mining

By Eibe Frank, Mark A. Hall, G. Holmes, Richard Kirkby, Bernhard Pfahringer, Ian H. Witten, Leonard E. Trigg. Data Mining and Knowledge Discovery Handbook, 2005

558 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/016fb4a9312b8b7fcf4fe1d96b85054cc52cc5cb

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In Artificial Intelligence

1. Petr Sojka
2. Radim Rehurek

Citations in the corpus

• Current and future applications of statistical machine learning algorithms for agricultural machine vision systems (2019, background): “Previous review of ML approaches exposed in agricultural data mining (Garner et al., 1995; Frank et al., 2009), plant stress phenotyping (Singh et al.”
• A clustering-based approach for discovering interesting places in trajectories (2008, methodology): “Weka-GDPM is an extension of Weka [8], which is a data mining toolkit that implements several al­gorithms for association rule mining, clustering, and classi­.cation.”
• Software Framework for Topic Modelling with Large Corpora (2010, methodology): “These include NLTK (Bird and Loper, 2004), Apache's UIMA and ClearTK (Ogren et al., 2008), Weka (Frank et al., 2005), OpenNLP (Baldridge et al., 2002), Mallet (McCallum, 2002), MDP (Zito et al., 2008), Nieme (Maes, 2009), Gate (Cunningham, 2002), Orange (Demšar et al., 2004) and many others.”