Abstract:

Being able to provide clear specifications of large datasets comprising hundreds of variables, each of which can take on many different values, while still being able to efficiently and accurately learn functional relations from such data would certainly make data mining techniques even more viable in the real world. In this report we describe a new modelling approach, which essentially generalizes discrete decision tree learning to induction of non-recursive functions over algebraic datatypes. Taking the CONFMAN mediation database as guiding example, we demonstrate how this approach allows us to give more natural data specifications that can take into account semantic aspects which are hard or even impossible to model in common attribute-value representations. We will also explain how this can have a positive impact on accuracy and efficiency.

• Introduction
• Improving existing specifications
  • Local improvements
  • Global improvements
• Design hints
  • Top-down design
  • Treatment of missing values
• Parametric polymorphism for more flexible data specifications
• Conclusion
• Acknowledgements
• References

This document was translated from L^AT_EX by H^EV^EA and H^AC^HA.