Multiple Self-Organizing Maps (MSOM) is an unsupervised learning technique in the field of artificial intelligence and machine learning.

An MSOM consists of the combination of multiple self-organizing maps, which are artificial neural networks used for data analysis and visualization of high-dimensional data. Each self-organizing map is trained with a different part of the input dataset, allowing the identification of different patterns and features in the data.

These are built in a cascading fashion, so that you have multiple layers where each uses the output information from the previous layers. In this way, as you move towards the end, the information takes on a higher level meaning.

The MSOM technique is commonly used in the analysis of large unstructured data sets, such as images, audio signals and text. MSOMs allow effective visualization of data in multiple dimensions, which facilitates the identification of patterns and trends.

MSOMs are also used in pattern recognition applications, such as anomaly detection in sensor data or identification of distinctive features in medical images. The combination of multiple self-organizing maps enables the identification of multiple patterns in different parts of the dataset, which can improve the accuracy and efficiency of analysis.