The other plots each overlay the resulting map with predicted values on an input dimension: red means a predicted 'yes' vote on that bill, blue means a 'no' vote. This makes SOMs useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling.
The artificial neural network introduced by the Finnish professor Teuvo Kohonen in the 1980s is sometimes called a Kohonen map or network.
Useful extensions include using toroidal grids where opposite edges are connected and using large numbers of nodes.
It has been shown that while self-organizing maps with a small number of nodes behave in a way that is similar to K-means, larger self-organizing maps rearrange data in a way that is fundamentally topological in character. In a square grid, for instance, we might consider the closest 4 or 8 nodes (the Von Neumann and Moore neighborhoods, respectively), or six nodes in a hexagonal grid. In maps consisting of thousands of nodes, it is possible to perform cluster operations on the map itself.
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Self-organizing maps differ from other artificial neural networks as they apply competitive learning as opposed to error-correction learning (such as backpropagation with gradient descent), and in the sense that they use a neighborhood function to preserve the topological properties of the input space. The first plot shows the grouping when the data are split into two clusters.
The input data was a table with a row for each member of Congress, and columns for certain votes containing each member's yes/no/abstain vote.
The usual arrangement of nodes is a two-dimensional regular spacing in a hexagonal or rectangular grid.