Volume 3 Supplement 1

4th German Conference on Chemoinformatics: 22. CIC-Workshop

Open Access

Explorative Data Analysis: from machine learning to discovery support systems

  • MR Berthold1, 2
Chemistry Central Journal20093(Suppl 1):O5

DOI: 10.1186/1752-153X-3-S1-O5

Published: 05 June 2009

Classic Data Analysis is based on the assumption that the type of models or patterns sought for – such as association rules or decision trees – is known in advance. In reality, however, users often do not have sufficient insights into the underlying system to be able to limit the choice of models appropriately before the data analysis process begins. Worse, still, the users often do not even know what types of patterns they would consider interesting.

Explorative Data Analysis addresses this concern by fostering a close interaction with the user, allowing her to continuously and quickly change model types and analysis focus throughout the data integration and analysis process.

In this talk I will present a number of explorative data analysis methods for life science related discovery tasks that have been developed within the KNIME Information Mining platform.

Authors’ Affiliations

(1)
University of Konstanz
(2)
KNIME.com GmbH

Copyright

© Berthold; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd.