# Self-Organizing Maps

## What are Self-Organizing Maps?

Self-Organizing Maps (SOMs) are a type of Neural Network that are used for unsupervised learning, particularly for visualizing and clustering high-dimensional data. SOMs are also known as Kohonen maps, after their inventor, Teuvo Kohonen.

The basic architecture of a SOM consists of a two-dimensional grid of nodes, where each node represents a specific feature or characteristic of the input data. During training, each input data point is mapped to the closest node on the grid, and the weights of the nodes are adjusted to minimize the distance between the input data and the nodes.

SOMs are particularly well-suited for tasks such as clustering, where the goal is to group similar data points together, and visualization, where the goal is to represent high-dimensional data in a lower-dimensional space. By mapping the input data to a two-dimensional grid, SOMs enable intelligent systems to visualize and analyze complex datasets in a more intuitive and interpretable way.

SOMs are used in a wide range of applications, including data mining, image processing, and bioinformatics. They are a powerful tool for unsupervised learning, enabling intelligent systems to discover and represent patterns and relationships in the input data without the need for explicit labels or annotations.

## Examples of Self-Organizing Maps

An example of Self-Organizing Maps (SOMs) is a model that is trained to cluster and visualize high-dimensional data, such as a dataset of customer preferences.

The SOM consists of a two-dimensional grid of nodes, where each node represents a specific feature or characteristic of the input data. During training, each input data point is mapped to the closest node on the grid, and the weights of the nodes are adjusted to minimize the distance between the input data and the nodes.

Once the SOM is trained, it can be used to visualize the input data in a lower-dimensional space. Each node on the grid represents a cluster of similar data points, and the position of the nodes on the grid represents the similarity between the clusters.

For example, a SOM could be used to analyze a dataset of customer preferences for a retail store. The input data would consist of features such as age, gender, income, and purchasing habits. The SOM would be trained to cluster the data into groups of similar customers, and the position of the nodes on the grid would represent the similarity between the customer groups.

The SOM could then be used to visualize the customer preferences in a more intuitive and interpretable way, such as a heat map or a scatter plot. This could help the retail store to better understand their customers and tailor their marketing strategies to specific customer groups.

SOMs are also used in other applications, such as image processing, where they can be used to cluster and visualize images based on their features, making them a powerful tool for a wide range of applications.