From black and white to full colour: extending redescription mining outside the Boolean world

Esther Galbrun (1, 2) , Pauli Miettinen (3)

1 HIIT - Helsinki Institute for Information Technology
2 Helsingin yliopisto = Helsingfors universitet = University of Helsinki
3 Max Planck Institute for Informatics [Saarbrücken]

Redescription mining is a powerful data analysis tool that is used to find multiple descriptions of the same entities. Consider geographical regions as an example. They can be characterized by the fauna that inhabits them on one hand and by their meteorological conditions on the other hand. Finding such redescriptors, a task known as niche-finding, is of much importance in biology. But current redescription mining methods cannot handle other than Boolean data. This restricts the range of possible applications or makes discretization a prerequisite, entailing a possibly harmful loss of information. In niche-finding, while the fauna can be naturally represented using a Boolean presence/absence data, the weather cannot. In this paper, we extend redescription mining to real-valued data using a surprisingly simple and efficient approach. We provide extensive experimental evaluation to study the behaviour of the proposed algorithm. Furthermore, we show the statistical significance of our results using recent innovations on randomization methods.

Domaines: Intelligence artificielle [cs.AI]

Esther Galbrun, Pauli Miettinen. From black and white to full colour: extending redescription mining outside the Boolean world. Proceedings of the 11th SIAM International Conference on Data Mining, SDM'11, Apr 2011, Phoenix, AZ, United States. ⟨10.1137/1.9781611972818.47⟩. ⟨hal-01399265⟩