Data mining in bioinformatics using Weka

Authors:
Eibe Frank;Mark Hall;Len Trigg;Geoffrey Holmes;Ian H. Witten
Affiliations:
Department of Computer Science, University of Waikato, Private Bag 3105, Hamilton, New Zealand;Department of Computer Science, University of Waikato, Private Bag 3105, Hamilton, New Zealand;Reel Two, PO Box 1538, Hamilton, New Zealand;Department of Computer Science, University of Waikato, Private Bag 3105, Hamilton, New Zealand;Department of Computer Science, University of Waikato, Private Bag 3105, Hamilton, New Zealand
Venue:
Bioinformatics
Year:
2004

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Abstract

Summary: The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection---common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data pre-processing methods complemented by graphical user interfaces for data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it. Availability: http://www.cs.waikato.ac.nz/ml/weka