has_specified_output achieves_planned_objective dataset creating clustered data set self-organizing map A self-organizing map (SOM) is an artificial neural network with objective class discovery that uses a neighborhood function to preserve the topological properties of a dataset to produce low-dimensional (typically 2) discretized representation of the training data set. A set of artificial neurons learn to map points in an input space to coordinates in an output space. The input space can have different dimensions and topology from the output space, and the SOM will attempt to preserve these. James Malone PERSON: Ryan Brinkman Ryan Brinkman SOM self-organizing map topologically preserved clustered data set class discovery data transformation class discovery objective pending final vetting