Data

Weka – collection of machine learning algorithms for data mining tasks

Scientific

Weka (Waikato Environment for Knowledge Analysis) is a comprehensive popular suite of machine learning software, developed at the University of Waikato, New Zealand. It is a collection of machine learning algorithms for solving real-world data mining problems including decision trees, support vector machines, instance-based classifiers, Bayes decision schemes, neural networks etc. and clustering.

The algorithms can either be applied directly to a dataset or called from your own Java code.

The Weka workbench 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.

Features include:

  • Provides an environment with algorithms for data preprocessing, feature selection, classification, regression, and clustering.
  • Graphical user interface that makes applying machine learning algorithms easy
    • Four graphical user interface modules:
      • Explorer.
      • Experimenter.
      • Knowledge Flow.
      • Simple Command Line Interface.
  • Schemes for classification include:
    • Decision trees, rule learners, naive Bayes, decision tables, locally weighted regression, SVMs, instance-based learners, logistic regression, voted perceptrons, multi-layer perceptron.
  • Schemes for numeric prediction include:
    • Lnear regression, model tree generators, locally weighted regression, instance-based learners, decision tables, multi-layer perceptron.
  • Meta-schemes include:
    • Bagging, boosting, stacking, regression via classification, classification via regression, cost sensitive classification.
  • Schemes for clustering:
    • EM and Cobweb.
  • Schemes for feature selection.
  • Provides implementations of learning algorithms:
    • Classification.
    • Clustering.
    • Association Rule Mining.
    • Attribute Selection.
  • General API to embed WEKA in other applications.

Website: www.cs.waikato.ac.nz/ml/weka
Support:
Developer: University of Waikato
License: GNU General Public License v2.0

Weka is written in Java. Learn Java with our recommended free books and free tutorials.

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