| Students will be familiar with at least one common data mining tool, so that they can competently apply it to various types of data mining problems, such as classification (k5), clustering (k5), association and classification rule mining (k5), and preprocessing (k5). Particular focus is put on correct evaluation of trained models (k6).
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Topics covered include
- Data mining process models
- Pre-processing techniques
- Inductive rule learning
- Efficient similarity-based techniques
- Association rule mining
- Stream Mining
- Evaluation
using common data mining software, such as Weka, KNIME, RapidMiner, Orange, or similar.
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