K-means vs SOM (2)

Topology:

K-means: K-means does not consider topological relationships between data points and focuses only on the location of cluster centers.

SOM: SOM considers the topological relationship between data points on a grid of neurons, preserving the spatial information between neighboring neurons.

Applicable scenarios:

K-means: Suitable for spherical, equal-sized clusters with similar density.

SOM: Suitable for capturing topological structures and nonlinear relationships in data, especially for visualization of high-dimensional data.