Titlebook: Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization; Dedicated to the Mem Jan Faigl,Madalina

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D. Thanoon,M. Garbey,B. L. Bass. However, these are usually scattered across independent libraries, making their integration and comparative application in data exploration difficult. Additionally, few visualizations focus on the dynamics of data forming a time series, while the importance of understanding data evolution is gaini

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E. Brian Butler,Paul E. Sovelius,Nancy Huynho their ability to create new possibly meaningful representations of materials from the explored space of examples. In this brief study, we investigate the relative usefulness of different representations of small organic molecules using a variational autoencoder model. Exploratory visualization of

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Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data VisualizationDedicated to the Mem

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,Machine Learning and?Data-Driven Approaches in?Spatial Statistics: A Case Study of?Housing Price Esaphical, economical and infrastructural data in order to bring out the socio-spatial structure of a city and then use this cluster information into the spatial diffusion process of the GWR. SOM gives a notion of proximity between clusters and thus provides a multi-scale degree of similarity (unlike

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Computational Surgery and Dual Trainingnce in the context of a sequential implementation. In the perspective of hardware implementations, we propose here a parallel version of FastBMU, and we analyse its behavior and its performance. Based on the performed analysis, we finally derive principles of a parallel hardware structure that maxim

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Computational Surgery and Dual Trainingaphical, economical and infrastructural data in order to bring out the socio-spatial structure of a city and then use this cluster information into the spatial diffusion process of the GWR. SOM gives a notion of proximity between clusters and thus provides a multi-scale degree of similarity (unlike

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