- The goal of SOM is to find prototype vectors that represent the input vectors and realize a continuous mapping from the high-dimensional input space to a grid of nodes.
- The cluster is represented by a node with an associated weight vector.
- A similarity measure is computed to determine the matching nodes of input vectors. Along with those of neighboring nodes, the weight-vectors of matching nodes are updated by the input vectors which are different from K-means.
- By iterative training, the model vectors learn to represent the probability distribution of the data in an orderly fashion.
- When the training is complete, clusters identified by mapping all data points to the output neurons and similar clusters are placed near each other.
- Please be attention that a random initialization of the weight vectors may result in instability of the clustering results.