Elisa Fromont and Marie-Odile Cordier and René Quiniou Extraction de connaissances provenant de données multisources pour la caractérisation d'arythmies cardiaques , Chapitre Fouille de données complexes , Vol. RNTI-E-4 , 25-45 , 2005 Abstract In many applications dealing with industrial or medical supervision, data are temporal time series related to numerous sensors that provide information which is complementary but also often redundant. We investigate the problem of learning, by inductive logic programming, symbolic rules that characterize cardiac arrhythmias from multisource data such as electrocardiograms or arterial blood pressure measures. A first strategy consists in aggregating the data and then in learning directly from these transformed data. This method is not very efficient and it is difficult to implement, especially designing the learning bias, when the amount of data is big. We propose an efficient two-step strategy that uses monosource learning to automatically bias and reduce the search space for multisource learning. The results obtained with this method are analyzed and compared to those obtained with the naive learning method. We show that an order of magnitude is gained on learning times with the new method. This web site is maintained by René Quiniou using the Weave system from the Caravel project: http://www-caravel.inria.fr. Last modification: 10-07-2011 09:54:25 Version française