Shum, Wing-HoWing-HoShumJin, Hui-DongHui-DongJinProf. LEUNG Kwong SakWong, Man-LeungMan-LeungWong2023-03-282023-03-282002Proceedings - IEEE International Conference on Data Mining, ICDM, 2002, pp. 434 - 4410769517544978-076951754-415504786http://hdl.handle.net/20.500.11861/7640The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. However, due to the dimensional conflict, the neighborhood preservation cannot always lead to perfect topology preservation. In this paper, we establish an Expanding SOM (ESOM) to detect and preserve better topology correspondence between the two spaces. Our experiment results demonstrate that the ESOM constructs better mappings than the classic SOM in terms of both the topological and the quantization errors. Furthermore, clustering results generated by the ESOM are more accurate than those by the SOM. © 2002 IEEE.enData VisualizationSelf-Organizing MapSelf-organizing MapQuantization ErrorNeural NetworkLearning ProcessLearning RateScatter PlotExcitatory NeuronsWeight VectorRegular GridRandom ValuesData SpaceLearnable ParametersOutput NeuronsLearning RulePhase ErrorClusters In DatasetNeighborhood RelationshipPeak SequencesReal-Life DatasetsOutput GridNeighborhood FunctionConference Paper10.1109/ICDM.2002.1183939