is proposed for community detection in dynamic social networks, which explores self-organizing maps (SOM) for cluster …

-Organizing Maps (SOM) in identifying clusters from EEG signals that could then be mapped to emotional classes. Beginning by training … varying sizes of SOM with the EEG data provided from the public dataset: DEAP. The produced graphs showing Neighbor Distance … identify correlations between the label and the clusters produced by the SOM. The results show that there is a potential of …

demonstrated is that the convergence index captures the notion that a SOM has learned the multivariate distribution of a training …

temporal thresholds. The adopted method, the Neural Network Self-Organizing Maps (SOM), is able to single out groups of cities …. The second section explains the data which will be used for the analysis and describes the used SOM algorithm. The … characteristics. The conclusions discuss the nature of those patterns. Due to SOM implementation it has been possible to identify …

Model (SOM), and a mapping to the Business Process Modelling Notation (BPMN) is shown. …

effectiveness of SOM as a tool to visualize the characteristics that lead to successful paths and the survival of firms. …