Self Organizing Map

Self-Organizing Maps (SOMs) are unsupervised neural networks designed to create low-dimensional representations of high-dimensional data while preserving topological relationships. Current research focuses on enhancing SOMs for improved accuracy and interpretability in various applications, including time series prediction, anomaly detection, and data clustering, often incorporating them into hybrid models with other machine learning techniques like autoencoders, radial basis function networks, and k-means clustering. These advancements are driving progress in diverse fields, from industrial process monitoring and medical image analysis to cybersecurity and space physics, by enabling efficient data visualization, dimensionality reduction, and pattern recognition in complex datasets.