Applied Descriptive Pattern Mining

Tutorial : Applied Descriptive Pattern Mining

Pattern mining, and more specifically descriptive pattern mining aims at extracting relevant "nuggets" of knowledge from a set of data.
In this context, the focus is on descriptive applications, i.e., for explorative data mining, e.g., extracting knowledge for humans,
instead of considering mainly automatic approaches, e.g., for classification.
Prominent approaches for descriptive pattern mining include frequent pattern mining, supervised descriptive rule induction,
and subgroup discovery but also descriptive community mining approaches.

The aim of this tutorial is to provide a general, comprehensive overview of the state-of-the-art of descriptive pattern mining.
We consider different types of data such as structured (tabular) data, texts, and networks.

The main contributions of this tutorial are the following:

  • We provide a comprehensive overview on descriptive pattern set mining techniques.
  • We present an in-depth introduction into a set of mining algorithms for different data types and show their relations.
  • We discuss a range of applications providing showcases for the introduced algorithms.

The Tutorial is presented by

  • Martin Atzmüller (University of Kassel)
  • Florian Lemmerich (University of Würzburg)

Date: Friday 30. Sep 2011 afternoon