Distance Metrics for Time-Series Data with Concentric Multi-Sphere Self Organizing Maps
Self-Organizing Maps have been shown to be a powerful unsupervised learning a tool in the analysis of complex high dimensional data. SOMs are capable of performing topological mapping, clustering and dimensionality reduction in order to effectively visual
|Collections||ANU Research Publications|
|Source:||Lecture Notes in Computer Science (LNCS)|
|01_Gedeon_Distance_Metrics_for_2013.pdf||570.81 kB||Adobe PDF||Request a copy|
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