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Self-Organizing Maps (SOM)

A Self-Organizing Map (SOM) is a type of unsupervised neural network used for dimensionality reduction and visualization of multidimensional data. It was developed by Teuvo Kohonen in the 1980s, and hence it is also known as a Kohonen Map. SOMs map high-dimensional data into a lower-dimensional representation, usually one or two dimensions, preserving the topological relationships of the original data.

Applications of SOMs

Data Visualization:

  • Reduce the dimensionality of complex data to facilitate its visualization and understanding.

Exploratory Data Analysis:

  • Discover structures and patterns in multidimensional datasets.

Clustering:

  • Group data into clusters in an unsupervised manner.

Pattern Recognition:

  • Identify and classify patterns in data, such as in image and signal analysis.

Data Compression:

  • Reduce the size of data while maintaining its structure and main characteristics.

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