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In this paper, Associations algorithms and Support Vector Machines (SVM) are applied to analyse years of solar catalogues data and to study the associations between eruptive filaments/prominences and Coronal Mass Ejections (CMEs). The aim is to identify patterns of associations that can be represented using SVM learning rules to enable real-time and reliable CME predictions. The NGDC filaments catalogue and the SOHO/LASCO CMEs catalogue are processed to associate filaments with CMEs based on timing and location information. Automated systems are created to process and associate years of filaments and CME data, which are later arranged in numerical training vectors and fed to machine learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Features representing the filament time, duration, type and extent are extracted from all the associated (A) and not-associated (NA) filaments and converted to a numerical format that is suitable for machine learning use. The machine learning system predicts if the filament is likely to initiate a CME. Intensive experiments are carried out to optimise the SVM. The prediction performance of SVM is analysed and recommendations for enhancing the performance are provided.