Self-Organizing Maps

What are Self-Organizing Maps (SOM)? SOM Explained

Self-Organizing Maps (SOM), also known as Kohonen maps, are unsupervised machine learning models used for clustering and visualization of high-dimensional data. SOMs are a type of artificial neural network that aims to map input data onto a lower-dimensional grid or lattice while preserving the topological relationships between the input data.

Here are the key characteristics and steps involved in the Self-Organizing Maps algorithm:

Network Architecture: A SOM consists of a grid of nodes, also called neurons or units, arranged in a two-dimensional lattice. Each node represents a prototype or reference vector in the input space.

Weight Initialization: The weights of the nodes are initialized with random values or sometimes using techniques like Principal Component Analysis (PCA) to preserve the variance of the input data.

Training Process: The SOM training process involves iterative updates to adjust the weights of the nodes to match the input data. The training follows the following steps:

a. Input Data: An input data point is randomly selected from the dataset.

b. Best Matching Unit (BMU): The node with the weights that best match the input data is identified. This is typically done by computing the Euclidean distance or other similarity measures between the input data and the node weights.

c. Neighborhood Function: The neighborhood of the BMU is defined, typically using a Gaussian or radial basis function centered at the BMU. The neighborhood function determines the extent to which the neighboring nodes’ weights are updated.

d. Weight Update: The weights of the BMU and its neighboring nodes are updated based on the input data and the neighborhood function. The weights are adjusted in the direction of the input data to better represent the distribution and structure of the input space.

Topology Preservation: One of the key advantages of SOMs is their ability to preserve the topological relationships between the input data. Nodes that are close to each other in the lattice represent similar features or patterns, allowing for effective clustering and visualization of the data.

Clustering and Visualization: After the training process, the SOM can be used for clustering or visualization purposes. Each node in the lattice represents a cluster or a region in the input space. By assigning input data points to the best-matching nodes, clustering can be performed. Furthermore, the lattice can be visualized to reveal the structure and relationships in the high-dimensional data.

SOMs have found applications in various domains, such as data exploration, dimensionality reduction, feature extraction, and pattern recognition. They are particularly useful for understanding the underlying structure and patterns in complex datasets and providing insights into the relationships among the data points.