Self-Organizing Maps (SOM) are useful for visualizing the structure and similarity of high-dimensional data by mapping it onto a 2D space.

 

Self-Organizing Maps (SOM) are useful for visualizing the structure and similarity of high-dimensional data by mapping it onto a 2D space. They are effective in exploring trends, such as customer purchase patterns or document features. In contrast, clustering methods are designed to clearly classify data into distinct groups—for example, segmenting customers into three categories or detecting anomalies. SOM is suited for understanding continuous relationships, while clustering is best when clear categorization or separation is required. Choosing between them depends on whether visualization or classification is the main goal.