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Data Mining - The PRONEL Method
ESPRIT 4 Project 28932, Fourth Framework Programme The aim of the PRONEL project was to develop a prototype of a data mining tool that were able to extracting Bayesian network models from data in "collaboration" with an domain expert. This was achieved and a software prototype was developed. This prototype is available for download. Pronel © is an European project ESPRIT performed by four societies: Siemens AG (Germany), Schlumberger (France), Hugin (Denmark), and Tiga Technologies (France). With the Pronel BN Learner tool you can load a database table and where the columns represent variables/nodes in the Bayesian network. Then, you can perform a structural learning task that will give you a possibly incomplete Bayesian network structure which may contain some uncertainty. Through simple interactions with the user, the uncertainties are solved and the final Bayesian network structure is determined. The last part of the process consist of learning the underlying probabilities of the Bayesian network.
Download Pronel User Guide (PDF) The functionality of the Pronel BN learner demo has been included in the Hugin tool. Project DetailsDuration: 18 months Coordinator: Partners: |
