Sparse weighted K-means for groups of mixed-type variables -- Fast parallel search of Best Matching Units in Self-Organizing Maps -- Neural networks for spatial models -- Machine Learning and Data-Driven Approaches in Spatial Statistics : a case study of housing price estimation -- Modification of the Classification-by-Component Predictor Using Dempster-Shafer-Theory -- Inferring epsilon-nets of Finite Sets in a RKHS -- Steps Forward to Quantum Learning Vector Quantization for Classification Learning on a Theoretical Quantum Computer -- Application of Kohonen Maps in Predicting and Characterizing VAT Fraud in Southern Mozambique -- Visual insights from the latent space of generative models for molecular design.

In this collection, the reader can find recent advancements in self-organizing maps (SOMs) and learning vector quantization (LVQ), including progressive ideas on exploiting features of parallel computing. The collection is balanced in presenting novel theoretical contributions with applied results in traditional fields of SOMs, such as visualization problems and data analysis. Besides, the collection further includes less traditional deployments in trajectory clustering and recent results on exploiting quantum computation. The presented book is worth interest to data analysis and machine learning researchers and practitioners, specifically those interested in being updated with current developments in unsupervised learning, data visualization, and self-organization.