K-means vs SOM (3)

Iterative process:

K-means: Updates the cluster centers by iteratively calculating the distance from data points to the nearest cluster center.

SOM: Iteratively adjusts the weights of neurons through competitive learning and neighborhood adjustment.

Sensitivity to initial values:

K-means: Sensitive to the choice of initial clustering centers, different initial values may lead to different results.

SOM: Relatively less sensitive to the choice of initial weights, but the initial network topology needs to be chosen carefully.

Interpretability:

K-means: results are more intuitive, with each cluster represented by a center point.

SOM: Provides a topology of the data that can be used to visualize and understand high dimensional data.[label][/label][checkbox checked=”true/false”][/checkbox]