Self-organizing maps

Both k-means and PCA can cluster the input data; however, they do not maintain topological relationship. In this section we will consider Self-organized maps (SOM), sometimes known as Kohonen networks or Winner take all units (WTU). They maintain the topological relation. SOMs are a very special kind of neural network, inspired by a distinctive feature of the human brain. In our brain, different sensory inputs are represented in a topologically ordered manner. Unlike other neural networks, neurons are not all connected to each other via weights; instead, they influence each other's learning. The most important aspect of SOM is that neurons represent the learned inputs in a topographic manner. They were proposed by Tuevo Kohonen in 1989 [2].

In SOMs, neurons are usually placed at nodes of a (1D or 2D) lattice. Higher dimensions are also possible but are rarely used in practice. Each neuron in the lattice is connected to all the input units via a weight matrix...