More Data Mining with Weka

This course follows on from Data Mining with Weka and provides a deeper account of data mining tools and techniques. Again the emphasis is on principles and practical data mining using Weka, rather than mathematical theory or advanced details of particular algorithms. Students will work with multimillion-instance datasets, classify text, experiment with clustering, association rules, neural networks, and much more.

A new session starts on 4 January 2017, and the course will run in self-paced unsupported mode until 3rd April; whereupon Statements of Completion will be produced and mailed out. Students should have completed Data Mining with Weka, or have equivalent knowledge of the subject.

The course features:

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Prof Ian H. Witten
Department of Computer Science
University of Waikato


  • Pre-course survey

  • Class 1 - Exploring Weka's interfaces, and working with big data

  • Class 2 - Discretization and text classification

  • Mid-course assessment

  • Class 3 - Classification rules, association rules, and clustering

  • Class 4 - Selecting attributes and counting the cost

  • Class 5 - Neural networks, learning curves, and performance optimization

  • Post-course assessment

  • Post-course survey