If the answer is “no”, please get this tutorial of Algobeans.
The term ‘self-organizing map’ might conjure up a militaristic image of data points marching towards their contingents on a map, which is a rather apt analogy of how the algorithm actually works.
A self-organizing map (SOM) is a clustering technique that helps you uncover categories in large datasets, such as to find customer profiles based on a list of past purchases. It is a special breed of unsupervised neural networks, where neurons (also called nodes or reference vectors) are arranged in a single, 2-dimensional grid, which can take the shape of either rectangles or hexagons.
HOW DOES SOM WORK?
In a nutshell, an SOM comprises neurons in the grid, which gradually adapt to the intrinsic shape of our data. The final result allows us to visualize data points and identify clusters in a lower dimension.
So how does the SOM grid learn the shape of our data? Well, this is done in an iterative process, which is summarized in the following steps, and visualized in the animated GIF below:
Step 0: Randomly position the grid’s neurons in the data space.
Step 1: Select one data point, either randomly or systematically cycling through the dataset in order
Step 2: Find the neuron that is closest to the chosen data point. This neuron is called the Best Matching Unit (BMU).
Step 3: Move the BMU closer to that data point. The distance moved by the BMU is determined by a learning rate, which decreases after each iteration.
Step 4: Move the BMU’s neighbors closer to that data point as well, with farther away neighbors moving less. Neighbors are identified using a radius around the BMU, and the value for this radius decreases after each iteration.
Step 5: Update the learning rate and BMU radius, before repeating Steps 1 to 4. Iterate these steps until positions of neurons have been stabilized.