Data mining in bioinformatics using Weka

A free java software for data mining by Frank E et al., University of Waikato, New Zealand.

Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Weka is open source software issued under the GNU General Public License.

Reference:

Data mining in bioinformatics using Weka.

Frank E, Hall M, Trigg L, Holmes G, Witten IH.

Department of Computer Science, University of Waikato, Private Bag 3105, Hamilton, New Zealand. eibe@cs.waikato.ac.nz

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.

PMID: 15073010 [PubMed - indexed for MEDLINE]

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Last update 11-Sep-2007, Rating Fair of 2 votes.