Kadim TaÅŸdemirTasdemir, Kadim2025-10-0620089789819698936, 9789819698042, 9789819698110, 9789819698905, 9789819512324, 9783032026019, 9783032008909, 9783031915802, 9789819698141, 97830319841369783540889052354088905116113349, 030297431611-33490302-974310.1007/978-3-540-88906-9_232-s2.0-58049118934https://www.scopus.com/inward/record.uri?eid=2-s2.0-58049118934&doi=10.1007%2F978-3-540-88906-9_23&partnerID=40&md5=60894eddb9a4609270768266cd268588https://gcris.yasar.edu.tr/handle/123456789/10362https://doi.org/10.1007/978-3-540-88906-9_23The Self-Organizing Map (SOM) which projects a (high-dimensional) data manifold onto a lower-dimensional (usually 2-d) rigid lattice is a commonly used manifold learning algorithm. However a postprocessing - that is often done by interactive visualization schemes - is necessary to reveal the knowledge of the SOM. Thanks to the SOM property of producing (ideally) a topology preserving mapping existing visualization schemes are often designed to show the similarities local to the lattice without considering the data topology. This can produce inadequate tools to investigate the detailed data structure and to what extent the topology is preserved during the SOM learning. A recent graph based SOM visualization CONNvis [1] which exploits the underutilized knowledge of data topology can be a suitable tool for such investigation. This paper discusses that CONNvis can represent the data topology on the SOM lattice despite the rigid grid structure and hence can show the topology preservation of the SOM and the extent of topology violations. © 2008 Springer Berlin Heidelberg. © 2021 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessConformal Mapping, Data Visualization, Learning Algorithms, Self Organizing Maps, Visualization, Data Manifolds, Graph-based Visualization, Grid Structures, High-dimensional, Interactive Visualizations, Manifold Learning Algorithm, Topology Preservation, Topology-preserving Mappings, TopologyConformal mapping, Data visualization, Learning algorithms, Self organizing maps, Visualization, Data manifolds, Graph-based visualization, Grid structures, High-dimensional, Interactive visualizations, Manifold learning algorithm, Topology preservation, Topology-preserving mappings, TopologyConference Object