Self-Organizing Maps - A Tutorial

Self-organizing maps (SOMs) are a data visualization technique invented by Professor Teuvo Kohonen which reduce the dimensions of data through the use of self-organizing neural networks. The problem that data visualization attempts to solve is that humans simply cannot visualize high dimensional data, so techniques are created to help us understand this high dimensional data. Two other techniques of reducing the dimensions of data that has been presented in this course has been N-Land and Multi-dimensional Scaling. The way SOMs go about reducing dimensions is by producing a map of usually 1 or 2 dimensions which plot the similarities of the data by grouping similar data items together. So SOMs accomplish two things, they reduce dimensions and display similarities.