How it works:

K-means: K-means is an iterative, distance-based clustering algorithm. It iterates until convergence by assigning data points to the cluster centers closest to them and updating the cluster centers.

SOM: SOM is an unsupervised learning algorithm that works based on competitive learning. In SOM, a neuron grid is defined and the data points are mapped onto this grid and clusters are formed by competitive learning and adjustment of neighborhood relations.

Model Structure:

K-means: K-means divides the data points into K clusters, each represented by a cluster center.

SOM: SOM uses a two-dimensional lattice of neurons, where each neuron represents a cluster and neighboring neurons are similar in the feature space.

[label][/label][checkbox checked=”true/false”][/checkbox]