Self-Organized Clustering

Self-Organized Clustering is an unsupervised learning method based on Self-Organizing Maps (SOM), which aims to automatically identify patterns and structures within a dataset through a neural network, achieving efficient data clustering. This method can map high-dimensional data to a lower-dimensional space while preserving the topological relationships of the data, making it suitable for visualization and classification tasks involving large-scale data. In the context of Miscellaneous tasks, Self-Organized Clustering holds significant application value in fields such as data mining, image processing, and bioinformatics.