Kohonen Networks, also known as Self-Organizing Maps (SOMs), are unsupervised neural networks used for clustering and visualization of high-dimensional data by mapping it onto a lower-dimensional grid. They are particularly effective in pattern recognition tasks, where the network learns to organize input data based on similarity without requiring labeled outputs.

Kohonen Network

Kohonen Networks

Self-organizing Maps (SOMs) are a type of unsupervised neural network that uses competitive learning to produce a low-dimensional, discretized representation of input data, preserving the topological properties of the input space. They are particularly useful for visualizing high-dimensional data and clustering tasks, often applied in fields like data mining and pattern recognition.

Self-Organizing Maps

SOMs

unsupervised neural networks

Clustering is an unsupervised learning technique used to group similar data points together based on specific characteristics or features, allowing for the discovery of patterns or structures within datasets. It is widely used in various fields such as data mining, image analysis, and market research to simplify data and make informed decisions.

clustering

visualization of high-dimensional data

lower-dimensional grid

pattern recognition tasks

Organizing input data

Similarity is a fundamental concept in mathematics and science, referring to the degree of resemblance between two objects, patterns, or sets of data, often used to identify relationships, patterns, or predict outcomes. It is crucial in fields like geometry, where similar figures have proportional sides and identical angles, and in machine learning, where similarity measures help in clustering and classification tasks.

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