There is only one question related to this in stackoverflow, and it is more about which one is better. I just dont really understand the difference. I mean they both work with vectors, which are assigned randomly to clusters, they both work with the centroids of the different clusters in order to determine the winning output node. I mean, where exactly lies the difference?
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问题:
回答1:
In K-means the nodes (centroids) are independent from each other. The winning node gets the chance to adapt each self and only that. In SOM the nodes (centroids) are placed onto a grid and so each node is consider to have some neighbors, the nodes adjacent or near to it in repspect with their position on the grid. So the winning node not only adapts itself but causes a change for its neighbors also. K-Means can be considered a special case of SOM were no neighbors are taken into account when modifing centroids vectors. For more, you can still google it ....
