Self-Organizing Maps (SOMs), also known as Kohonen maps, are a type of unsupervised neural network used for data visualization and clustering. They are particularly useful for visualizing high-dimensional data in a lower-dimensional space, such as 2D or 3D.
The key feature of SOMs is their ability to map high-dimensional data onto a lower-dimensional space while preserving the topological relationships between the data points. This is achieved by using a competitive learning algorithm to train the network, where each neuron in the network competes to represent a subset of the input data.
During training, the SOM gradually organizes the input data into a map, where nearby neurons in the map represent similar data points. This allows the SOM to be used for clustering and visualization of the data, making it a useful tool for exploratory data analysis and pattern recognition.
SOMs have been used in a variety of applications, including image and speech recognition, text mining, and bioinformatics. They are often used in combination with other machine learning algorithms, such as clustering and classification, to improve the accuracy and interpretability of the results.
One limitation of SOMs is that they are often computationally intensive and require significant computational resources to train on large datasets. However, they remain a valuable tool for exploratory data analysis and visualization, particularly in applications where high-dimensional data must be analyzed and interpreted.